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2311.01343
| 43 |
3.4.2 Masked Prompting with Multinomial Head. To achieve this purpose, we first design a masked prompting strategy to gen- erate recommendation-oriented prompts. For each user, we ran- domly mask the interacted items rð by 100 Ã ðð%, where the re- maining items are denoted as rððð ððð , and use it to generate a ð ððð,ð recommendation-oriented prompt x . All the hold-out items, ð which we denote with a multi-hot vector râððð , are treated as the ððð,ð target. The prompt x ð
(c) Recommendation Prompts & Target (prompt) <user_ð> has interacted with <item_ð â²> <item_ð â²> the user will interact with: (target) râððð
|
2311.01343#43
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
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{
"id": "2308.10053"
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{
"id": "2307.02046"
},
{
"id": "2306.05817"
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{
"id": "2205.08084"
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{
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},
{
"id": "2305.07001"
},
{
"id": "2303.13835"
},
{
"id": "2303.18223"
},
{
"id": "2302.03735"
},
{
"id": "2305.00447"
},
{
"id": "2305.08845"
},
{
"id": "2104.08691"
},
{
"id": "2308.10837"
},
{
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] |
{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 43,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "3.4.2 Masked Prompting with Multinomial Head. To achieve this purpose, we first design a masked prompting strategy to gen- erate recommendation-oriented prompts. For each user, we ran- domly mask the interacted items rð by 100 Ã ðð%, where the re- maining items are denoted as rððð ððð , and use it to generate a ð ððð,ð recommendation-oriented prompt x . All the hold-out items, ð which we denote with a multi-hot vector râððð , are treated as the ððð,ð target. The prompt x ð\n(c) Recommendation Prompts & Target (prompt) <user_ð> has interacted with <item_ð â²> <item_ð â²> the user will interact with: (target) râððð",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 44 |
which triggers the reasoning ability of the pretrained LLM by using relational phrase "has interacted with" to describe the historical interactions, and using the phrase "the user will interact with" to guide the prediction of the target items râððð
We name CLLM4Rec in the finetuning stage as RecLLM, which inherits the CLLM4Rec base model lim, from the collaborative LLM in the pretraining stage and introduces a new item prediction head with multinomial likelihood, ie., frec, whose weights are also tied with the item token embeddings Z!â. The generation of the hold hold-out items r/°"â via the RecLLM can be formulated as follows: rhold ~ multi (free (nie? ,) , Npold) , where he = limy (x/*°?) 5
rhold ~ multi (free (nie? ,) , Npold) , where he = limy (x/*°?) 5 (9)
|
2311.01343#44
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
[
{
"id": "2302.13971"
},
{
"id": "2206.07682"
},
{
"id": "2308.10053"
},
{
"id": "2307.02046"
},
{
"id": "2306.05817"
},
{
"id": "2205.08084"
},
{
"id": "2303.14524"
},
{
"id": "2305.07001"
},
{
"id": "2303.13835"
},
{
"id": "2303.18223"
},
{
"id": "2302.03735"
},
{
"id": "2305.00447"
},
{
"id": "2305.08845"
},
{
"id": "2104.08691"
},
{
"id": "2308.10837"
},
{
"id": "2305.06569"
}
] |
{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 44,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "which triggers the reasoning ability of the pretrained LLM by using relational phrase \"has interacted with\" to describe the historical interactions, and using the phrase \"the user will interact with\" to guide the prediction of the target items râððð\nWe name CLLM4Rec in the finetuning stage as RecLLM, which inherits the CLLM4Rec base model lim, from the collaborative LLM in the pretraining stage and introduces a new item prediction head with multinomial likelihood, ie., frec, whose weights are also tied with the item token embeddings Z!â. The generation of the hold hold-out items r/°\"â via the RecLLM can be formulated as follows: rhold ~ multi (free (nie? ,) , Npold) , where he = limy (x/*°?) 5\nrhold ~ multi (free (nie? ,) , Npold) , where he = limy (x/*°?) 5 (9)",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 45 |
rhold ~ multi (free (nie? ,) , Npold) , where he = limy (x/*°?) 5 (9)
(9) where ðð¢ðð¡ð denotes the multinomial distribution and ð âððð is the number of hold-out items for user ð. When finetuning the RecLLM according to Eq. (9), hððð ð,ð,â1, which can be viewed as the user la- tent variable summarizing the historical interaction of user ð, is encouraged to be similar to the collaborative embeddings of all the interacted items. In addition, we keep it regularized with the content LLM in a similar manner as Eq. (7), and use the stochastic ððð,ð 6. Through item reordering strategy to generate the prompt x ð the proposed finetuning strategy, CLLM4Rec can fully utilize the encoded knowledge from the pretrained LLM backbone and the
6The objective of the RecLLM is formulated in Eq. (10) in Appendix A.2.
|
2311.01343#45
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
[
{
"id": "2302.13971"
},
{
"id": "2206.07682"
},
{
"id": "2308.10053"
},
{
"id": "2307.02046"
},
{
"id": "2306.05817"
},
{
"id": "2205.08084"
},
{
"id": "2303.14524"
},
{
"id": "2305.07001"
},
{
"id": "2303.13835"
},
{
"id": "2303.18223"
},
{
"id": "2302.03735"
},
{
"id": "2305.00447"
},
{
"id": "2305.08845"
},
{
"id": "2104.08691"
},
{
"id": "2308.10837"
},
{
"id": "2305.06569"
}
] |
{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 45,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "rhold ~ multi (free (nie? ,) , Npold) , where he = limy (x/*°?) 5 (9)\n(9) where ðð¢ðð¡ð denotes the multinomial distribution and ð âððð is the number of hold-out items for user ð. When finetuning the RecLLM according to Eq. (9), hððð ð,ð,â1, which can be viewed as the user la- tent variable summarizing the historical interaction of user ð, is encouraged to be similar to the collaborative embeddings of all the interacted items. In addition, we keep it regularized with the content LLM in a similar manner as Eq. (7), and use the stochastic ððð,ð 6. Through item reordering strategy to generate the prompt x ð the proposed finetuning strategy, CLLM4Rec can fully utilize the encoded knowledge from the pretrained LLM backbone and the\n6The objective of the RecLLM is formulated in Eq. (10) in Appendix A.2.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 47 |
# 3.5 Predictions with CLLM4Rec
After the pretraining and finetuning of CLLM4Rec, to make recom- mendation for user ð, we can convert the whole historical interac- tions of the user, i.e., rð , into the recommendation-oriented prompt ððð,ð Ëx as described in Section 3.4.2 (with no masked items) and input ð it into the RecLLM model. Then, the multinomial probability Ërð over all ð½ items can be obtained through one forward propagation via = Ëðððð Ërð = ðð¢ðð¡ð , where uninteracted items with top-ð scores in Ërð can be selected as recommendations.
# 4 EMPIRICAL STUDY
In this section, we present the experiments on four public datasets and one LinkedIn dataset to demonstrate the effectiveness of CLLM4Rec, aiming to answer the following research questions.
|
2311.01343#47
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 47,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 3.5 Predictions with CLLM4Rec\nAfter the pretraining and finetuning of CLLM4Rec, to make recom- mendation for user ð, we can convert the whole historical interac- tions of the user, i.e., rð , into the recommendation-oriented prompt ððð,ð Ëx as described in Section 3.4.2 (with no masked items) and input ð it into the RecLLM model. Then, the multinomial probability Ërð over all ð½ items can be obtained through one forward propagation via = Ëðððð Ërð = ðð¢ðð¡ð , where uninteracted items with top-ð scores in Ërð can be selected as recommendations.\n# 4 EMPIRICAL STUDY\nIn this section, we present the experiments on four public datasets and one LinkedIn dataset to demonstrate the effectiveness of CLLM4Rec, aiming to answer the following research questions.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 48 |
In this section, we present the experiments on four public datasets and one LinkedIn dataset to demonstrate the effectiveness of CLLM4Rec, aiming to answer the following research questions.
RQ1. How does CLLM4Rec, the first RS that tightly couples the ID-based paradigm with the LLM-based paradigm, perform compared to state-of-the-art ID-based and LLM-based RSs? ⢠RQ2. How does the pretraining stage of CLLM4Rec (including the mutual regularization trick and the stochastic item reorder strategy) influence the performance of CLLM4Rec?
⢠RQ3. How does the finetuning stage of CLLM4Rec with masked prompt and multinomial item prediction head influence the efficiency and effectiveness of recommendations.
# 4.1 Experimental Setup
|
2311.01343#48
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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] |
{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 48,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "In this section, we present the experiments on four public datasets and one LinkedIn dataset to demonstrate the effectiveness of CLLM4Rec, aiming to answer the following research questions.\nRQ1. How does CLLM4Rec, the first RS that tightly couples the ID-based paradigm with the LLM-based paradigm, perform compared to state-of-the-art ID-based and LLM-based RSs? ⢠RQ2. How does the pretraining stage of CLLM4Rec (including the mutual regularization trick and the stochastic item reorder strategy) influence the performance of CLLM4Rec?\n⢠RQ3. How does the finetuning stage of CLLM4Rec with masked prompt and multinomial item prediction head influence the efficiency and effectiveness of recommendations.\n# 4.1 Experimental Setup",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 49 |
# 4.1 Experimental Setup
4.1.1 Datasets. The experiments are mainly based on four pub- lic datasets: Amazon (AM)-Beauty dataset, AM-Toys dataset, AM- Sports dataset [17] and the Yelp dataset [38], where we binarize the interactions by keeping only ratings > 3 and treat them as implicit feedback [39]. In addition, we filter the dataset such that they keep the original 5-core property after binarization. For each user, we randomly select 80% of interactions for training, 10% for validation, and 10% for testing, where as least one item is selected in the valida- tion and the test set. The reviews that users provide to the items are collected as the textual feature xð¢ð£ ð ð . The real-world experiments are based on a job recommendation dataset collected nearline at the Company, where userâs click on the job Ads are logged as the implicit feedback, and usersâ self-provided biography xð¢ ð and the job descriptions xð£ ð are collected as the textual features, respectively. The statistics of the dataset are summarized in Table 3 in Appendix.
|
2311.01343#49
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 49,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 4.1 Experimental Setup\n4.1.1 Datasets. The experiments are mainly based on four pub- lic datasets: Amazon (AM)-Beauty dataset, AM-Toys dataset, AM- Sports dataset [17] and the Yelp dataset [38], where we binarize the interactions by keeping only ratings > 3 and treat them as implicit feedback [39]. In addition, we filter the dataset such that they keep the original 5-core property after binarization. For each user, we randomly select 80% of interactions for training, 10% for validation, and 10% for testing, where as least one item is selected in the valida- tion and the test set. The reviews that users provide to the items are collected as the textual feature xð¢ð£ ð ð . The real-world experiments are based on a job recommendation dataset collected nearline at the Company, where userâs click on the job Ads are logged as the implicit feedback, and usersâ self-provided biography xð¢ ð and the job descriptions xð£ ð are collected as the textual features, respectively. The statistics of the dataset are summarized in Table 3 in Appendix.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 50 |
Implementation Details. Due to the space limitation, we 4.1.2 only discuss CLLM4Rec with GPT-2 backbone with token embed- ding 768 and token size 50,257 in this section, where experiments with T5 backbone are discussed in Appendix B. During the train- ing stage, we first optimize the content LLM as Eq. (5) via lan- guage modeling for 10 epochs to warm up the user/item content token embeddings. Then, in the mutually-regularized pretraining stage, we alternatively train the collaborative and content LLMs as specified in Eqs. (7) and (8) for 100 epochs. Finally, we conduct the recommendation-oriented finetuning for 150 epochs, where the RecLLM is monitored with metrics Recall@20, Recall@40, and
Collaborative Large Language Model for Recommender Systems
NDCG@100 calculated on the validation set as with [39]. RecLLM with the best performance are logged and evaluated on the test set as the final results. ðð in Eqs. (7) and (8) is an important hyper- parameter, we first fix its value to the optimal one found by grid search, and then discuss its influence in Section 4.3.
# 4.2 Comparison with Baselines
|
2311.01343#50
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 50,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Implementation Details. Due to the space limitation, we 4.1.2 only discuss CLLM4Rec with GPT-2 backbone with token embed- ding 768 and token size 50,257 in this section, where experiments with T5 backbone are discussed in Appendix B. During the train- ing stage, we first optimize the content LLM as Eq. (5) via lan- guage modeling for 10 epochs to warm up the user/item content token embeddings. Then, in the mutually-regularized pretraining stage, we alternatively train the collaborative and content LLMs as specified in Eqs. (7) and (8) for 100 epochs. Finally, we conduct the recommendation-oriented finetuning for 150 epochs, where the RecLLM is monitored with metrics Recall@20, Recall@40, and\nCollaborative Large Language Model for Recommender Systems\nNDCG@100 calculated on the validation set as with [39]. RecLLM with the best performance are logged and evaluated on the test set as the final results. ðð in Eqs. (7) and (8) is an important hyper- parameter, we first fix its value to the optimal one found by grid search, and then discuss its influence in Section 4.3.\n# 4.2 Comparison with Baselines",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 51 |
# 4.2 Comparison with Baselines
4.2.1 Baselines. To demonstrate the multifaceted superiority of the proposed CLLM4Rec, we include the following ID-based and (L)LM-based RSs as the baselines for comparisons:
# ID-based Baselines.
Multi-Vae [39] is an ID-based collaborative filtering baseline that recommends new items by reconstructing the ratings rð via a variational auto-encoder (VAE) with multinomial likelihood. ⢠Md-Cvae [40] is a hybrid RS that extends the Multi-VAE by ð ð to reguintroducing a dual feature VAE on textual features xð¢ð£ larize the reconstruction of rð in the Multi-VAE.
# LM-based Baselines7.
⢠Bert4Rec [41] uses masked language modeling (MLM) pro- posed in BERT [32] to learn user/item embeddings for recom- mendation with bidirectional self-attention mechanism.
⢠S3Rec [38] extends BERT4Rec by augmenting the MLM with auxiliary tasks such as item attribute prediction, where content features can be fused for self-supervised learning.
# LLM-based Baselines. (a) Qualitative Analysis.
|
2311.01343#51
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 51,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 4.2 Comparison with Baselines\n4.2.1 Baselines. To demonstrate the multifaceted superiority of the proposed CLLM4Rec, we include the following ID-based and (L)LM-based RSs as the baselines for comparisons:\n# ID-based Baselines.\nMulti-Vae [39] is an ID-based collaborative filtering baseline that recommends new items by reconstructing the ratings rð via a variational auto-encoder (VAE) with multinomial likelihood. ⢠Md-Cvae [40] is a hybrid RS that extends the Multi-VAE by ð ð to reguintroducing a dual feature VAE on textual features xð¢ð£ larize the reconstruction of rð in the Multi-VAE.\n# LM-based Baselines7.\n⢠Bert4Rec [41] uses masked language modeling (MLM) pro- posed in BERT [32] to learn user/item embeddings for recom- mendation with bidirectional self-attention mechanism.\n⢠S3Rec [38] extends BERT4Rec by augmenting the MLM with auxiliary tasks such as item attribute prediction, where content features can be fused for self-supervised learning.\n# LLM-based Baselines. (a) Qualitative Analysis.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
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] |
2311.01343
| 52 |
# LLM-based Baselines. (a) Qualitative Analysis.
Both pseudo-ID-based and description-based methods discussed in Section 2.2 represent user/item with multiple tokens and formu- late direct recommendation as a token generation problem. Since the generated tokens could be irrelevant to the recommendation purpose, candidate items usually need to be explicitly provided in the prompt (e.g., P5 [20] provides 100 candidate items where one is positive, and TALLRec [36] outputs yes/no decision based on user/item descriptions in the prompts, etc.). In contrast, CLLM4Rec can generate multiple recommendations from the entire candidate pool. Therefore, these methods cannot directly work in our setting, and the comparisons are mainly based on qualitative analysis. (b) Quantitative Analysis
In addition, we design the following LLM-based baselines to
quantitatively demonstrate the effectiveness of CLLM4Rec. ⢠Llm-Scratch has the same structure as CLLM4Rec, but it trains the whole model from scratch instead of loading and fixing the weights of the pretrained LLM backbone.
⢠Llm-CF eliminates the content LLM from CLLM4Rec and the mutually-regularized pretraining step and uses only the collabo- rative LLM and RecLLM for recommendation.
|
2311.01343#52
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 52,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# LLM-based Baselines. (a) Qualitative Analysis.\nBoth pseudo-ID-based and description-based methods discussed in Section 2.2 represent user/item with multiple tokens and formu- late direct recommendation as a token generation problem. Since the generated tokens could be irrelevant to the recommendation purpose, candidate items usually need to be explicitly provided in the prompt (e.g., P5 [20] provides 100 candidate items where one is positive, and TALLRec [36] outputs yes/no decision based on user/item descriptions in the prompts, etc.). In contrast, CLLM4Rec can generate multiple recommendations from the entire candidate pool. Therefore, these methods cannot directly work in our setting, and the comparisons are mainly based on qualitative analysis. (b) Quantitative Analysis\nIn addition, we design the following LLM-based baselines to\nquantitatively demonstrate the effectiveness of CLLM4Rec. ⢠Llm-Scratch has the same structure as CLLM4Rec, but it trains the whole model from scratch instead of loading and fixing the weights of the pretrained LLM backbone.\n⢠Llm-CF eliminates the content LLM from CLLM4Rec and the mutually-regularized pretraining step and uses only the collabo- rative LLM and RecLLM for recommendation.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
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] |
2311.01343
| 53 |
⢠Llm-FTALL has the same structure as CLLM4Rec, but it fine- tunes the whole network including the vocab embeddings as well as other parts of the pretrained LLM, instead of training only the newly-introduced user/item token embeddings.
7Note that both Bert4Rec and S3Rec are original designed for sequential recommenda- tion. In this paper, we use similar recommendation-oriented finetuning as CLLM4Rec to adapt them to direct recommendation, where item sequences generated from masked interactions are used to predict all hold-out items with multinomial likelihood.
Conferenceâ17, July 2017, Washington, DC, USA
# Table 1: Comparison between CLLM4Rec and various base- lines with GPT-backbone on three Amazon Review datasets.
|
2311.01343#53
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 53,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "⢠Llm-FTALL has the same structure as CLLM4Rec, but it fine- tunes the whole network including the vocab embeddings as well as other parts of the pretrained LLM, instead of training only the newly-introduced user/item token embeddings.\n7Note that both Bert4Rec and S3Rec are original designed for sequential recommenda- tion. In this paper, we use similar recommendation-oriented finetuning as CLLM4Rec to adapt them to direct recommendation, where item sequences generated from masked interactions are used to predict all hold-out items with multinomial likelihood.\nConferenceâ17, July 2017, Washington, DC, USA\n# Table 1: Comparison between CLLM4Rec and various base- lines with GPT-backbone on three Amazon Review datasets.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
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] |
2311.01343
| 54 |
AM-Beauty Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.1295 0.1472 0.1126 0.1354 0.1720 0.2058 0.1677 0.1789 0.0835 0.0976 0.0781 0.0867 LLM-Scratch LLM-CF LLM-FtAll LLM-FixOrd LLM-PreRec 0.0840 0.1319 0.1335 0.1524 0.1547 0.1265 0.1841 0.1988 0.2219 0.2196 0.0583 0.0855 0.0836 0.1072 0.1051 CLLM4Rec 0.1656 0.2323 0.1118 AM-Toys Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.1076 0.1291 0.0853 0.1064 0.1558 0.1804 0.1375 0.1524 0.0781 0.0844 0.0532 0.0665 LLM-Scratch LLM-CF LLM-FtAll LLM-FixOrd LLM-PreRec 0.0485 0.1027 0.1162
|
2311.01343#54
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 54,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "AM-Beauty Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.1295 0.1472 0.1126 0.1354 0.1720 0.2058 0.1677 0.1789 0.0835 0.0976 0.0781 0.0867 LLM-Scratch LLM-CF LLM-FtAll LLM-FixOrd LLM-PreRec 0.0840 0.1319 0.1335 0.1524 0.1547 0.1265 0.1841 0.1988 0.2219 0.2196 0.0583 0.0855 0.0836 0.1072 0.1051 CLLM4Rec 0.1656 0.2323 0.1118 AM-Toys Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.1076 0.1291 0.0853 0.1064 0.1558 0.1804 0.1375 0.1524 0.0781 0.0844 0.0532 0.0665 LLM-Scratch LLM-CF LLM-FtAll LLM-FixOrd LLM-PreRec 0.0485 0.1027 0.1162",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
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] |
2311.01343
| 55 |
0.0665 LLM-Scratch LLM-CF LLM-FtAll LLM-FixOrd LLM-PreRec 0.0485 0.1027 0.1162 0.1342 0.1308 0.0771 0.1434 0.1542 0.1887 0.1859 0.0362 0.0680 0.0696 0.0889 0.0874 CLLM4Rec 0.1436 0.1933 0.0918 AM-Sports Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.0659 0.0714 0.0521 0.0616 0.0975 0.1180 0.0701 0.0813 0.0446 0.0514 0.0305 0.0438 LLM-Scratch LLM-CF LLM-FtAll LLM-FixOrd LLM-PreRec 0.0362 0.0642 0.0794 0.0901 0.0839 0.0538 0.0966 0.1002 0.1295 0.1248 0.0362 0.0419 0.0424 0.0592 0.0561 CLLM4Rec 0.0926 0.1351 0.0634
|
2311.01343#55
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 55,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "0.0665 LLM-Scratch LLM-CF LLM-FtAll LLM-FixOrd LLM-PreRec 0.0485 0.1027 0.1162 0.1342 0.1308 0.0771 0.1434 0.1542 0.1887 0.1859 0.0362 0.0680 0.0696 0.0889 0.0874 CLLM4Rec 0.1436 0.1933 0.0918 AM-Sports Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.0659 0.0714 0.0521 0.0616 0.0975 0.1180 0.0701 0.0813 0.0446 0.0514 0.0305 0.0438 LLM-Scratch LLM-CF LLM-FtAll LLM-FixOrd LLM-PreRec 0.0362 0.0642 0.0794 0.0901 0.0839 0.0538 0.0966 0.1002 0.1295 0.1248 0.0362 0.0419 0.0424 0.0592 0.0561 CLLM4Rec 0.0926 0.1351 0.0634",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
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] |
2311.01343
| 56 |
⢠Llm-FixOrd has the same structure as CLLM4Rec but it removes the stochastic item reordering strategy for both the collaborative LLM in pretraining and the RecLLM in finetuning.
⢠Llm-PreRec discards finetuning and ranks the categorical prob- ability from the next item token prediction head of the collabora- tive LLM in the pretraining stage to make recommendations.
4.2.2 Results on the Public Datasets. We first analyze the ex- perimental results on four public datasets to provide preliminary answers for RQs. 1, 2, 3. From Tables 1 and 2, we can find that the ID-base method, Multi-VAE, remains a strong baseline for col- laborative filtering (CF). LLM-CF, the CF backbone of CLLM4Rec, cannot beat Multi-VAE on both AM-Sports and Toys datasets, even if the "hard" part of the prompt triggers the reasoning ability of the pretrained LLM. However, when large textual data are avail- able, CLLM4Rec outperforms its ID-based counterpart, MD-CVAE (which tightly couples an item content VAE with the Multi-VAE)
Conferenceâ17, July 2017, Washington, DC, USA
Table 2: Comparison between CLLM4Rec and various base- lines on the Yelp dataset and the Company dataset.
|
2311.01343#56
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 56,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "⢠Llm-FixOrd has the same structure as CLLM4Rec but it removes the stochastic item reordering strategy for both the collaborative LLM in pretraining and the RecLLM in finetuning.\n⢠Llm-PreRec discards finetuning and ranks the categorical prob- ability from the next item token prediction head of the collabora- tive LLM in the pretraining stage to make recommendations.\n4.2.2 Results on the Public Datasets. We first analyze the ex- perimental results on four public datasets to provide preliminary answers for RQs. 1, 2, 3. From Tables 1 and 2, we can find that the ID-base method, Multi-VAE, remains a strong baseline for col- laborative filtering (CF). LLM-CF, the CF backbone of CLLM4Rec, cannot beat Multi-VAE on both AM-Sports and Toys datasets, even if the \"hard\" part of the prompt triggers the reasoning ability of the pretrained LLM. However, when large textual data are avail- able, CLLM4Rec outperforms its ID-based counterpart, MD-CVAE (which tightly couples an item content VAE with the Multi-VAE)\nConferenceâ17, July 2017, Washington, DC, USA\nTable 2: Comparison between CLLM4Rec and various base- lines on the Yelp dataset and the Company dataset.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 57 |
Conferenceâ17, July 2017, Washington, DC, USA
Table 2: Comparison between CLLM4Rec and various base- lines on the Yelp dataset and the Company dataset.
Yelp Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.0526 0.0664 0.0418 0.0563 0.0842 0.1058 0.0724 0.0893 0.0424 0.0497 0.0361 0.0485 LLM-Scratch LLM-CF LLM-FTAll LLM-FixOrd LLM-PreRec 0.0199 0.0541 0.0653 0.0694 0.0639 0.0325 0.0860 0.0989 0.1053 0.1021 0.0159 0.0412 0.0520 0.0524 0.0498 CLLM4Rec 0.0735 0.1149 0.0536 LinkedIn Recall@10 Recall@20 NDCG@10 Two-Tower 0.1186 0.2041 0.0979 M6-Retrieval CLLM4Rec-Emb CLLM4Rec 0.1279 0.1302 0.1427 0.2118 0.2165 0.2398 0.1020 0.1034 0.1199
|
2311.01343#57
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 57,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Conferenceâ17, July 2017, Washington, DC, USA\nTable 2: Comparison between CLLM4Rec and various base- lines on the Yelp dataset and the Company dataset.\nYelp Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.0526 0.0664 0.0418 0.0563 0.0842 0.1058 0.0724 0.0893 0.0424 0.0497 0.0361 0.0485 LLM-Scratch LLM-CF LLM-FTAll LLM-FixOrd LLM-PreRec 0.0199 0.0541 0.0653 0.0694 0.0639 0.0325 0.0860 0.0989 0.1053 0.1021 0.0159 0.0412 0.0520 0.0524 0.0498 CLLM4Rec 0.0735 0.1149 0.0536 LinkedIn Recall@10 Recall@20 NDCG@10 Two-Tower 0.1186 0.2041 0.0979 M6-Retrieval CLLM4Rec-Emb CLLM4Rec 0.1279 0.1302 0.1427 0.2118 0.2165 0.2398 0.1020 0.1034 0.1199",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
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] |
2311.01343
| 58 |
by a large margin. This is because MD-CVAE uses shallow bag- of-words to represent the textual features, for which pretrained LLMs in CLLM4Rec can provide deeper understanding via their pretrained knowledge. The importance of pretrained knowledge can also be shown by the LLM-Scratch model, which performs the worst among all included baselines. An interesting finding is that, LLM-FTAll, which finetunes the whole model including the pretrained LLM backbone, performs worse than CLLM4Rec, which optimizes only the newly introduced user/item token embeddings. The reason could be that, since the weights of the pretrained LLM are fully optimized, the recommendation-specific corpus is still not enough to adapt the pretrained LLM with good generalization ability for RS. Therefore, the cons of degenerating the pretrained knowledge outweigh the introduction of RS-specific knowledge. We can also find that LLM-PreRec, which uses the collaborative LLM in the pretraining stage to generate recommendations,is already a strong baseline. This demonstrates the effectiveness of the soft+hard prompting strategy, which facilitates efficient and stable language modeling on recommendation-oriented corpus with heterogeneous tokens. Still, CLLM4Rec performs better than LLM-PreRec, which shows the effectiveness of recommendation-oriented finetuning in adapting collaborative LLM for efficient recommendations.
|
2311.01343#58
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 58,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "by a large margin. This is because MD-CVAE uses shallow bag- of-words to represent the textual features, for which pretrained LLMs in CLLM4Rec can provide deeper understanding via their pretrained knowledge. The importance of pretrained knowledge can also be shown by the LLM-Scratch model, which performs the worst among all included baselines. An interesting finding is that, LLM-FTAll, which finetunes the whole model including the pretrained LLM backbone, performs worse than CLLM4Rec, which optimizes only the newly introduced user/item token embeddings. The reason could be that, since the weights of the pretrained LLM are fully optimized, the recommendation-specific corpus is still not enough to adapt the pretrained LLM with good generalization ability for RS. Therefore, the cons of degenerating the pretrained knowledge outweigh the introduction of RS-specific knowledge. We can also find that LLM-PreRec, which uses the collaborative LLM in the pretraining stage to generate recommendations,is already a strong baseline. This demonstrates the effectiveness of the soft+hard prompting strategy, which facilitates efficient and stable language modeling on recommendation-oriented corpus with heterogeneous tokens. Still, CLLM4Rec performs better than LLM-PreRec, which shows the effectiveness of recommendation-oriented finetuning in adapting collaborative LLM for efficient recommendations.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
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] |
2311.01343
| 59 |
4.2.3 Results on the Company Dataset. In the real-world exper- iments, we compare CLLM4Rec with the two-tower (TT) model uti- lized in the Company for job recommendations. The TT model is im- plemented as a two-branch multi-layer perceptron (MLP), where the input user/item embeddings include embeddings extracted from a graph neural network (GNN) learned on user-job bipartite graph, as well as features extracted from an internal BERT model. In addition, since the textual features are available for almost every user and item, we compare CLLM4Rec with the state-of-the-art LLM-based RS, M6-Retrieval [19], which takes the dimensional-reduced last- layer embeddings of user/item descriptions from M6 Transformer for contrastive recommendations. The results are summarized in Table 2. For Table 2, we can find that CLLM4Rec outperforms the
# Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1
(a) AM-Beauty Dataset (b) AM-Toys Dataset (c) AM-Sports Dataset (d) Yelp Dataset
|
2311.01343#59
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 59,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "4.2.3 Results on the Company Dataset. In the real-world exper- iments, we compare CLLM4Rec with the two-tower (TT) model uti- lized in the Company for job recommendations. The TT model is im- plemented as a two-branch multi-layer perceptron (MLP), where the input user/item embeddings include embeddings extracted from a graph neural network (GNN) learned on user-job bipartite graph, as well as features extracted from an internal BERT model. In addition, since the textual features are available for almost every user and item, we compare CLLM4Rec with the state-of-the-art LLM-based RS, M6-Retrieval [19], which takes the dimensional-reduced last- layer embeddings of user/item descriptions from M6 Transformer for contrastive recommendations. The results are summarized in Table 2. For Table 2, we can find that CLLM4Rec outperforms the\n# Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1\n(a) AM-Beauty Dataset (b) AM-Toys Dataset (c) AM-Sports Dataset (d) Yelp Dataset",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 60 |
(a) AM-Beauty Dataset (b) AM-Toys Dataset (c) AM-Sports Dataset (d) Yelp Dataset
Figure 4: Sensitivity analysis w.r.t. ðð , which controls the strength of mutual-regularization for CLLM4Rec.
shallow TT model by a large margin. However, although the in- ference latency for CLLM4Rec is significantly improved compared with existing methods due to the introduction of recommendation- oriented finetuning, directly deploying CLLM4Rec online is still infeasible, as the inference budgets are higher compared to the TT model. Therefore, we design the CLLM4Rec-Emb baseline, which includes the user/item token embeddings Zð,ð¢ and Zð,ð£ learned from CLLM4Rec (projected into 128 dimensions) as extra inputs for the TT model, which demonstrates a performance improvement than the original TT model and the M6-Retrieval model in our offline ex- periment. This demonstrates the potential application of CLLM4Rec in industrial applications where low latency matters.
|
2311.01343#60
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 60,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "(a) AM-Beauty Dataset (b) AM-Toys Dataset (c) AM-Sports Dataset (d) Yelp Dataset \nFigure 4: Sensitivity analysis w.r.t. ðð , which controls the strength of mutual-regularization for CLLM4Rec.\nshallow TT model by a large margin. However, although the in- ference latency for CLLM4Rec is significantly improved compared with existing methods due to the introduction of recommendation- oriented finetuning, directly deploying CLLM4Rec online is still infeasible, as the inference budgets are higher compared to the TT model. Therefore, we design the CLLM4Rec-Emb baseline, which includes the user/item token embeddings Zð,ð¢ and Zð,ð£ learned from CLLM4Rec (projected into 128 dimensions) as extra inputs for the TT model, which demonstrates a performance improvement than the original TT model and the M6-Retrieval model in our offline ex- periment. This demonstrates the potential application of CLLM4Rec in industrial applications where low latency matters.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 61 |
4.3 Parameter Sensitivity Analysis To further answer RQs. 2 and 3, we vary ðð in Eqs. (7), (8), and (10) that controls the strength of mutual regularization and investigates how it influences the performance of CLLM4Rec. From Fig. 4, we can find that, when ðð is small, the mutual regularization is weak, and content LLM cannot provide enough user/item content side in- formation to support the collaborative LLM and RecLLM. Therefore, the recommendation performance degenerates to a similar level as the LLM-CF. On the other hand, when ðð is too large, the MR loss in Eqs. (7), (8) and (10) dominates, which hinders CLLM4Rec from learning user/item token embeddings via language modeling and finetuning. Generally, for all four datasets, the performance of CLLM4Rec peaks at around ðð = 1, which serves as a good start when applying the GPT-based CLLM4Rec to new datasets.
# 5 CONCLUSION
|
2311.01343#61
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 61,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "4.3 Parameter Sensitivity Analysis To further answer RQs. 2 and 3, we vary ðð in Eqs. (7), (8), and (10) that controls the strength of mutual regularization and investigates how it influences the performance of CLLM4Rec. From Fig. 4, we can find that, when ðð is small, the mutual regularization is weak, and content LLM cannot provide enough user/item content side in- formation to support the collaborative LLM and RecLLM. Therefore, the recommendation performance degenerates to a similar level as the LLM-CF. On the other hand, when ðð is too large, the MR loss in Eqs. (7), (8) and (10) dominates, which hinders CLLM4Rec from learning user/item token embeddings via language modeling and finetuning. Generally, for all four datasets, the performance of CLLM4Rec peaks at around ðð = 1, which serves as a good start when applying the GPT-based CLLM4Rec to new datasets.\n# 5 CONCLUSION",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 62 |
# 5 CONCLUSION
In this paper, we proposed CLLM4Rec, the first method that tightly couples the ID paradigm and the LLM paradigm of RS, which faith- fully captures user/item semantics while fully utilizing encoded knowledge and logical reasoning ability of pretrained LLMs simul- taneously. Specifically, with mutually-regularized pretraining based on soft+hard prompting strategy, CLLM4Rec can effectively capture the user/item collaborative and content information via language modeling. Furthermore, with recommendation-oriented finetuning, the pretrained knowledge of CLLM4Rec can be fully utilized to efficiently generate recommendations. Extensive experiments show the multi-faceted superiority of CLLM4Rec over state-of-the-art.
Collaborative Large Language Model for Recommender Systems
REFERENCES [1] Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich. Recommender Systems: An Introduction. Cambridge University Press, 2010. [2] James Bennett, Stan Lanning, et al. The Netflix prize. In KDD CUP, volume 2007,
page 35, 2007.
|
2311.01343#62
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 62,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# 5 CONCLUSION\nIn this paper, we proposed CLLM4Rec, the first method that tightly couples the ID paradigm and the LLM paradigm of RS, which faith- fully captures user/item semantics while fully utilizing encoded knowledge and logical reasoning ability of pretrained LLMs simul- taneously. Specifically, with mutually-regularized pretraining based on soft+hard prompting strategy, CLLM4Rec can effectively capture the user/item collaborative and content information via language modeling. Furthermore, with recommendation-oriented finetuning, the pretrained knowledge of CLLM4Rec can be fully utilized to efficiently generate recommendations. Extensive experiments show the multi-faceted superiority of CLLM4Rec over state-of-the-art.\nCollaborative Large Language Model for Recommender Systems\nREFERENCES [1] Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich. Recommender Systems: An Introduction. Cambridge University Press, 2010. [2] James Bennett, Stan Lanning, et al. The Netflix prize. In KDD CUP, volume 2007,\npage 35, 2007.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 63 |
page 35, 2007.
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[5] Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, and Jason Weston. Starspace: Embed all the things! In AAAI, volume 32, 2018.
[6] Yehuda Koren, Steffen Rendle, and Robert Bell. Advances in collaborative filtering. Recommender systems handbook, pages 91â142, 2021.
[7] Pasquale Lops, Marco De Gemmis, and Giovanni Semeraro. Content-based rec- ommender systems: State of the art and trends. Recommender systems handbook, pages 73â105, 2011.
|
2311.01343#63
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 63,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "page 35, 2007.\n[3] Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. Where to go next for recommender systems? ID vs. modality- based recommender models revisited. arXiv preprint arXiv:2303.13835, 2023. [4] Andriy Mnih and Russ R Salakhutdinov. Probabilistic matrix factorization. In\nNeurIPS, volume 20, 2007.\n[5] Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, and Jason Weston. Starspace: Embed all the things! In AAAI, volume 32, 2018.\n[6] Yehuda Koren, Steffen Rendle, and Robert Bell. Advances in collaborative filtering. Recommender systems handbook, pages 91â142, 2021.\n[7] Pasquale Lops, Marco De Gemmis, and Giovanni Semeraro. Content-based rec- ommender systems: State of the art and trends. Recommender systems handbook, pages 73â105, 2011.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 64 |
[8] Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, and Jundong Li. Path- In SIGKDD, page specific counterfactual fairness for recommender systems. 3638â3649, 2023.
[9] Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. A survey of large language models. arXiv preprint arXiv:2303.18223, 2023.
[10] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Åukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NeurIPS, volume 30, 2017.
[11] Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. Improving language understanding by generative pre-training. 2018.
|
2311.01343#64
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 64,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "[8] Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, and Jundong Li. Path- In SIGKDD, page specific counterfactual fairness for recommender systems. 3638â3649, 2023.\n[9] Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. A survey of large language models. arXiv preprint arXiv:2303.18223, 2023.\n[10] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Åukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NeurIPS, volume 30, 2017.\n[11] Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. Improving language understanding by generative pre-training. 2018.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
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] |
2311.01343
| 65 |
[12] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR, 21(1):5485â5551, 2020.
[13] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. LlaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.
[14] Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682, 2022.
[15] Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, and Jundong Li. Knowledge editing for large language models: A survey, 2023.
|
2311.01343#65
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"id": "2206.07682"
},
{
"id": "2308.10053"
},
{
"id": "2307.02046"
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{
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{
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{
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},
{
"id": "2308.10837"
},
{
"id": "2305.06569"
}
] |
{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 65,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "[12] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR, 21(1):5485â5551, 2020.\n[13] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. LlaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.\n[14] Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682, 2022.\n[15] Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, and Jundong Li. Knowledge editing for large language models: A survey, 2023.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
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] |
2311.01343
| 66 |
[16] Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Jiliang Tang, and Qing Li. Recommender systems in the era of large language models (LLMs). arXiv preprint arXiv:2307.02046, 2023.
[17] Julian McAuley and Alex Yang. Addressing complex and subjective product- related queries with customer reviews. In WWW, pages 625â635, 2016.
[18] Yaochen Zhu and Zhenzhong Chen. Variational bandwidth auto-encoder for hybrid recommender systems. IEEE Transactions on Knowledge and Data Engi- neering, 35(5):5371â5385, 2022.
[19] Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. M6-rec: Generative pretrained language models are open-ended recommender systems. arXiv preprint arXiv:2205.08084, 2022.
[20] Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. Recommendation as language processing (RLP): A unified pretrain, personalized prompt & predict paradigm (P5). In Proceedings of the 16th ACM Conference on Recommender Systems, pages 299â315, 2022.
|
2311.01343#66
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 66,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "[16] Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Jiliang Tang, and Qing Li. Recommender systems in the era of large language models (LLMs). arXiv preprint arXiv:2307.02046, 2023.\n[17] Julian McAuley and Alex Yang. Addressing complex and subjective product- related queries with customer reviews. In WWW, pages 625â635, 2016.\n[18] Yaochen Zhu and Zhenzhong Chen. Variational bandwidth auto-encoder for hybrid recommender systems. IEEE Transactions on Knowledge and Data Engi- neering, 35(5):5371â5385, 2022.\n[19] Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. M6-rec: Generative pretrained language models are open-ended recommender systems. arXiv preprint arXiv:2205.08084, 2022.\n[20] Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. Recommendation as language processing (RLP): A unified pretrain, personalized prompt & predict paradigm (P5). In Proceedings of the 16th ACM Conference on Recommender Systems, pages 299â315, 2022.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
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] |
2311.01343
| 67 |
[21] Jiaxing Qu, Yuxuan Richard Xie, and Elif Ertekin. A language-based recommen- dation system for material discovery. In ICML, 2023.
[22] Lei Li, Yongfeng Zhang, and Li Chen. Personalized prompt learning for explain- able recommendation. ACM Transactions on Information Systems, 41(4):1â26,
Conferenceâ17, July 2017, Washington, DC, USA
2023.
[23] Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. Chat-rec: Towards interactive and explainable llms-augmented recom- mender system. arXiv preprint arXiv:2303.14524, 2023.
[24] Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. Large language models are zero-shot rankers for recom- mender systems. arXiv preprint arXiv:2305.08845, 2023.
|
2311.01343#67
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 67,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "[21] Jiaxing Qu, Yuxuan Richard Xie, and Elif Ertekin. A language-based recommen- dation system for material discovery. In ICML, 2023.\n[22] Lei Li, Yongfeng Zhang, and Li Chen. Personalized prompt learning for explain- able recommendation. ACM Transactions on Information Systems, 41(4):1â26,\nConferenceâ17, July 2017, Washington, DC, USA\n2023.\n[23] Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. Chat-rec: Towards interactive and explainable llms-augmented recom- mender system. arXiv preprint arXiv:2303.14524, 2023.\n[24] Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. Large language models are zero-shot rankers for recom- mender systems. arXiv preprint arXiv:2305.08845, 2023.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 68 |
[25] Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, and Ji- Rong Wen. Recommendation as instruction following: A large language model empowered recommendation approach. arXiv preprint arXiv:2305.07001, 2023.
[26] Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, and Julian McAuley. Large language models as zero-shot conversational recommenders. arXiv preprint arXiv:2308.10053, 2023.
[27] Fan Yang, Zheng Chen, Ziyan Jiang, Eunah Cho, Xiaojiang Huang, and Yanbin Lu. Palr: Personalization aware llms for recommendation. arXiv e-prints, pages arXivâ2305, 2023.
[28] Jianchao Ji, Zelong Li, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Juntao Tan, and Yongfeng Zhang. Genrec: Large language model for generative recommendation. arXiv e-prints, pages arXivâ2307, 2023.
|
2311.01343#68
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 68,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "[25] Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, and Ji- Rong Wen. Recommendation as instruction following: A large language model empowered recommendation approach. arXiv preprint arXiv:2305.07001, 2023.\n[26] Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, and Julian McAuley. Large language models as zero-shot conversational recommenders. arXiv preprint arXiv:2308.10053, 2023.\n[27] Fan Yang, Zheng Chen, Ziyan Jiang, Eunah Cho, Xiaojiang Huang, and Yanbin Lu. Palr: Personalization aware llms for recommendation. arXiv e-prints, pages arXivâ2305, 2023.\n[28] Jianchao Ji, Zelong Li, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Juntao Tan, and Yongfeng Zhang. Genrec: Large language model for generative recommendation. arXiv e-prints, pages arXivâ2307, 2023.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
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] |
2311.01343
| 69 |
[29] Zhixuan Chu, Hongyan Hao, Xin Ouyang, Simeng Wang, Yan Wang, Yue Shen, Jinjie Gu, Qing Cui, Longfei Li, Siqiao Xue, et al. Leveraging large language models for pre-trained recommender systems. arXiv preprint arXiv:2308.10837, 2023.
[30] Wenyue Hua, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. How to index item ids for recommendation foundation models. arXiv preprint arXiv:2305.06569, 2023.
[31] Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter- efficient prompt tuning. arXiv preprint arXiv:2104.08691, 2021.
[32] Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. BERT: pre- training of deep bidirectional transformers for language understanding. In Proceedings of NAACL, pages 4171â4186, 2019.
|
2311.01343#69
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 69,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "[29] Zhixuan Chu, Hongyan Hao, Xin Ouyang, Simeng Wang, Yan Wang, Yue Shen, Jinjie Gu, Qing Cui, Longfei Li, Siqiao Xue, et al. Leveraging large language models for pre-trained recommender systems. arXiv preprint arXiv:2308.10837, 2023.\n[30] Wenyue Hua, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. How to index item ids for recommendation foundation models. arXiv preprint arXiv:2305.06569, 2023.\n[31] Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter- efficient prompt tuning. arXiv preprint arXiv:2104.08691, 2021.\n[32] Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. BERT: pre- training of deep bidirectional transformers for language understanding. In Proceedings of NAACL, pages 4171â4186, 2019.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
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] |
2311.01343
| 70 |
[33] Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. Recent advances in natural language processing via large pre-trained language models: A survey. ACM Computing Surveys, 56(2):1â40, 2023.
[34] Peng Liu, Lemei Zhang, and Jon Atle Gulla. Pre-train, prompt and recommenda- tion: A comprehensive survey of language modelling paradigm adaptations in recommender systems. arXiv preprint arXiv:2302.03735, 2023.
[35] Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, et al. How can recommender systems benefit from large language models: A survey. arXiv preprint arXiv:2306.05817, 2023.
[36] Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. TallRec: An effective and efficient tuning framework to align large language model with recommendation. arXiv preprint arXiv:2305.00447, 2023.
|
2311.01343#70
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 70,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "[33] Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. Recent advances in natural language processing via large pre-trained language models: A survey. ACM Computing Surveys, 56(2):1â40, 2023.\n[34] Peng Liu, Lemei Zhang, and Jon Atle Gulla. Pre-train, prompt and recommenda- tion: A comprehensive survey of language modelling paradigm adaptations in recommender systems. arXiv preprint arXiv:2302.03735, 2023.\n[35] Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, et al. How can recommender systems benefit from large language models: A survey. arXiv preprint arXiv:2306.05817, 2023.\n[36] Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. TallRec: An effective and efficient tuning framework to align large language model with recommendation. arXiv preprint arXiv:2305.00447, 2023.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
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] |
2311.01343
| 71 |
[37] Yifan Hu, Yehuda Koren, and Chris Volinsky. Collaborative filtering for implicit feedback datasets. In IEEE International Conference on Data Mining, pages 263â 272, 2008.
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[39] Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. Varia- tional autoencoders for collaborative filtering. In WWW, pages 689â698, 2018. [40] Yaochen Zhu and Zhenzhong Chen. Mutually-regularized dual collaborative variational auto-encoder for recommendation systems. In WWW, pages 2379â 2387, 2022.
[41] Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. BERT4Rec: Sequential recommendation with bidirectional encoder representa- tions from transformer. In CIKM, pages 1441â1450, 2019.
Conferenceâ17, July 2017, Washington, DC, USA
|
2311.01343#71
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 71,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "[37] Yifan Hu, Yehuda Koren, and Chris Volinsky. Collaborative filtering for implicit feedback datasets. In IEEE International Conference on Data Mining, pages 263â 272, 2008.\n[38] Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. S3-Rec: Self-supervised learning for sequen- tial recommendation with mutual information maximization. In CIKM, pages 1893â1902, 2020.\n[39] Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. Varia- tional autoencoders for collaborative filtering. In WWW, pages 689â698, 2018. [40] Yaochen Zhu and Zhenzhong Chen. Mutually-regularized dual collaborative variational auto-encoder for recommendation systems. In WWW, pages 2379â 2387, 2022.\n[41] Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. BERT4Rec: Sequential recommendation with bidirectional encoder representa- tions from transformer. In CIKM, pages 1441â1450, 2019.\nConferenceâ17, July 2017, Washington, DC, USA",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 72 |
Conferenceâ17, July 2017, Washington, DC, USA
Table 3: Statistics of the datasets. #Feat. stands for number of textual features (i.e., # reviews for AM/Yelp datasets, and #user biography+#job descriptions for the LinkedIn dataset.
Dataset AM-Beauty AM-Toys AM-Sports Yelp LinkedIn #Int. 94,148 95,420 185,718 292,017 90,173 #Users 10, 553 11, 268 22, 686 28, 330 22, 391 #Items 6, 086 7, 309 12, 301 18, 775 1, 071 Sparsity 99.85% 99.88% 99.93% 99.94% 99.62% #Feat. 70,604 70,784 137,618 224,825 23,362
Table 4: Comparison between CLLM4Rec and various base- lines with T5-backbone on three Amazon Review datasets.
|
2311.01343#72
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 72,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "Conferenceâ17, July 2017, Washington, DC, USA\nTable 3: Statistics of the datasets. #Feat. stands for number of textual features (i.e., # reviews for AM/Yelp datasets, and #user biography+#job descriptions for the LinkedIn dataset.\nDataset AM-Beauty AM-Toys AM-Sports Yelp LinkedIn #Int. 94,148 95,420 185,718 292,017 90,173 #Users 10, 553 11, 268 22, 686 28, 330 22, 391 #Items 6, 086 7, 309 12, 301 18, 775 1, 071 Sparsity 99.85% 99.88% 99.93% 99.94% 99.62% #Feat. 70,604 70,784 137,618 224,825 23,362\nTable 4: Comparison between CLLM4Rec and various base- lines with T5-backbone on three Amazon Review datasets.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 73 |
AM-Beauty Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.1295 0.1472 0.1126 0.1354 0.1720 0.2058 0.1677 0.1789 0.0835 0.0976 0.0781 0.0867 CLLM4Rec-T5 CLLM4Rec 0.1538 0.1656 0.2105 0.2323 0.1052 0.1118 AM-Toys Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.1076 0.1291 0.0853 0.1064 0.1558 0.1804 0.1375 0.1524 0.0781 0.0844 0.0532 0.0665 CLLM4Rec-T5 CLLM4Rec 0.1328 0.1436 0.1840 0.1933 0.0851 0.0918 AM-Sports Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.0659 0.0714 0.0521 0.0616 0.0975 0.1180 0.0701 0.0813
|
2311.01343#73
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 73,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "AM-Beauty Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.1295 0.1472 0.1126 0.1354 0.1720 0.2058 0.1677 0.1789 0.0835 0.0976 0.0781 0.0867 CLLM4Rec-T5 CLLM4Rec 0.1538 0.1656 0.2105 0.2323 0.1052 0.1118 AM-Toys Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.1076 0.1291 0.0853 0.1064 0.1558 0.1804 0.1375 0.1524 0.0781 0.0844 0.0532 0.0665 CLLM4Rec-T5 CLLM4Rec 0.1328 0.1436 0.1840 0.1933 0.0851 0.0918 AM-Sports Recall@20 Recall@40 NDCG@100 Multi-VAE MD-CVAE BERT4Rec S3Rec 0.0659 0.0714 0.0521 0.0616 0.0975 0.1180 0.0701 0.0813",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
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] |
2311.01343
| 75 |
# A TECHNICAL DETAILS A.1 Implementation of Soft+Hard Prompting
To implement the soft+hard prompting strategy discussed in Section 3.3.2 for decoder-only LLMs such as GPT, we can generate only the "keys" and "values" for the heterogeneous tokens in the prompts ð,ð x , and use the "query" of the last token as a start to generate ð ð,ð the homogeneous tokens of the main texts x for language ð modeling. For encoder-decoder-based LLMs such as T5, a natural ð¢ð£,ð ð,ð thought is to input the prompts x in the encoder, and use ð ð ð ð,ð , x the decoder to generate the main texts x ð
# A.2 Recommendation-Oriented Finetuning
If we denote the multinomial probability obtained from the Re- cLLM prediction head ðððð as Ërâððð , and denote the stacked item
|
2311.01343#75
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 75,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# A TECHNICAL DETAILS A.1 Implementation of Soft+Hard Prompting\nTo implement the soft+hard prompting strategy discussed in Section 3.3.2 for decoder-only LLMs such as GPT, we can generate only the \"keys\" and \"values\" for the heterogeneous tokens in the prompts ð,ð x , and use the \"query\" of the last token as a start to generate ð ð,ð the homogeneous tokens of the main texts x for language ð modeling. For encoder-decoder-based LLMs such as T5, a natural ð¢ð£,ð ð,ð thought is to input the prompts x in the encoder, and use ð ð ð ð,ð , x the decoder to generate the main texts x ð\n# A.2 Recommendation-Oriented Finetuning\nIf we denote the multinomial probability obtained from the Re- cLLM prediction head ðððð as Ërâððð , and denote the stacked item",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 76 |
# Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1
collaborative token embeddings of items interacted by user i as zi, the rec-step objective of the recommendation-oriented finetuning (regularized with the content LLM) can be formulated as: MAP (Lu lv g)\ â hold) shold _ Al || tul|_ Ar || Lo Lrec_step (2) Zi 6) = â Sire Inf; ea âFF z; ia
# Ar || Lo z;
# ia
# Multinomial NLL Loss Ac | Le _ > 2 ee ~
# Prior loss
Ae ||_ Lu _ seul? Ac | Le _ scl)" SW 74 > 2 ee ~ Fpl], + Crees k
_ seul? Ac | Le _ scl)" 74 > 2 ee ~ Fpl], k MR loss with content LLM
|
2311.01343#76
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 76,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# Yaochen Zhuâ,1, Liang Wu2, Qi Guo2, Liangjie Hong2, Jundong Li1\ncollaborative token embeddings of items interacted by user i as zi, the rec-step objective of the recommendation-oriented finetuning (regularized with the content LLM) can be formulated as: MAP (Lu lv g)\\ â hold) shold _ Al || tul|_ Ar || Lo Lrec_step (2) Zi 6) = â Sire Inf; ea âFF z; ia\n# Ar || Lo z;\n# ia\n# Multinomial NLL Loss Ac | Le _ > 2 ee ~\n# Prior loss\nAe ||_ Lu _ seul? Ac | Le _ scl)\" SW 74 > 2 ee ~ Fpl], + Crees k\n_ seul? Ac | Le _ scl)\" 74 > 2 ee ~ Fpl], k MR loss with content LLM",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 77 |
_ seul? Ac | Le _ scl)" 74 > 2 ee ~ Fpl], k MR loss with content LLM
(10) where NLL stands for negative log-likelihood, and Cððð is the con- stant irrelevant for the optimization purpose. From the form of the multinomial NLL loss we can find that, when finetuning the RecLLM according to Eq. (10), the hððð ð,ð,â1 output by the CLLM4Rec Ëðððð , which can be viewed as the user latent variable base model summarizing the historical interaction of user ð, is encouraged to be similar to the collaborative embeddings of all the interacted items.
# B EXPERIMENTS B.1 Statistics of the Datasets
The statistics of the datasets are summarized in Table 3.
# B.2 Experiments on T5 Backbone
|
2311.01343#77
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
[
{
"id": "2302.13971"
},
{
"id": "2206.07682"
},
{
"id": "2308.10053"
},
{
"id": "2307.02046"
},
{
"id": "2306.05817"
},
{
"id": "2205.08084"
},
{
"id": "2303.14524"
},
{
"id": "2305.07001"
},
{
"id": "2303.13835"
},
{
"id": "2303.18223"
},
{
"id": "2302.03735"
},
{
"id": "2305.00447"
},
{
"id": "2305.08845"
},
{
"id": "2104.08691"
},
{
"id": "2308.10837"
},
{
"id": "2305.06569"
}
] |
{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 77,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "_ seul? Ac | Le _ scl)\" 74 > 2 ee ~ Fpl], k MR loss with content LLM\n(10) where NLL stands for negative log-likelihood, and Cððð is the con- stant irrelevant for the optimization purpose. From the form of the multinomial NLL loss we can find that, when finetuning the RecLLM according to Eq. (10), the hððð ð,ð,â1 output by the CLLM4Rec Ëðððð , which can be viewed as the user latent variable base model summarizing the historical interaction of user ð, is encouraged to be similar to the collaborative embeddings of all the interacted items.\n# B EXPERIMENTS B.1 Statistics of the Datasets\nThe statistics of the datasets are summarized in Table 3.\n# B.2 Experiments on T5 Backbone",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 78 |
# B EXPERIMENTS B.1 Statistics of the Datasets
The statistics of the datasets are summarized in Table 3.
# B.2 Experiments on T5 Backbone
Implementation. We adopt the T5-base model8 as the back- B.2.1 bone, which has 32,128 vocab tokens (the last 28 tokens are empty), where each token is associated with a 768-dimensional vocab em- bedding. Model training generally follows similar steps as the model with GPT-2 backbone described in Section 4.1.2, where we first warm up the content LLM as Eq. (5) for ten epochs. Then, we con- duct the mutually-regularized finetuning as Eqs. (7), (8) for 100 epoch, and conduct finetuning as Eq. (10) for 150 epochs.
|
2311.01343#78
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
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},
{
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{
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}
] |
{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 78,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "# B EXPERIMENTS B.1 Statistics of the Datasets\nThe statistics of the datasets are summarized in Table 3.\n# B.2 Experiments on T5 Backbone\nImplementation. We adopt the T5-base model8 as the back- B.2.1 bone, which has 32,128 vocab tokens (the last 28 tokens are empty), where each token is associated with a 768-dimensional vocab em- bedding. Model training generally follows similar steps as the model with GPT-2 backbone described in Section 4.1.2, where we first warm up the content LLM as Eq. (5) for ten epochs. Then, we con- duct the mutually-regularized finetuning as Eqs. (7), (8) for 100 epoch, and conduct finetuning as Eq. (10) for 150 epochs.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
|
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] |
2311.01343
| 79 |
B.2.2 Results & Analysis. The experimental results are summa- rized in Table 4. We can find that although CLLM4Rec with T5 back- bone generally outperforms ID-based and shallow LM-based base- lines, its performance is consistently worse than CLLM4Rec with GPT-2 backbone. The overall inferior performance of CLLM4Rec with T5 backbone can be two-fold. First, we note that the vocab embeddings in T5 are initialized with unit variance, whereas embed- dings in GPT-2 are initialized with a variance of 0.02. Therefore, the weights and embeddings in T5 has much larger numerical values, which leads to large update steps when errors are backpropagating from the outputs to the prompts. Therefore, the training is not as stable as the GPT-2 backbone. In addition, in the finetuning stage of the original T5 model, the prompts are generally used to guide the macro behavior of the model. e.g., changing the model behavior from question answering to machine generation via prompt "trans- late English to French". Therefore, another reason for the inferiority of T5 backbone could be the mismatch between the original T5 prompts and the prompts intended to be used in CLLM4Rec.
8https://huggingface.co/t5-base.
|
2311.01343#79
|
Collaborative Large Language Model for Recommender Systems
|
Recently, there is a growing interest in developing next-generation
recommender systems (RSs) based on pretrained large language models (LLMs),
fully utilizing their encoded knowledge and reasoning ability. However, the
semantic gap between natural language and recommendation tasks is still not
well addressed, leading to multiple issues such as spuriously-correlated
user/item descriptors, ineffective language modeling on user/item contents, and
inefficient recommendations via auto-regression, etc. In this paper, we propose
CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and
ID paradigm of RS, aiming to address the above challenges simultaneously. We
first extend the vocabulary of pretrained LLMs with user/item ID tokens to
faithfully model the user/item collaborative and content semantics.
Accordingly, in the pretraining stage, a novel soft+hard prompting strategy is
proposed to effectively learn user/item collaborative/content token embeddings
via language modeling on RS-specific corpora established from user-item
interactions and user/item features, where each document is split into a prompt
consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and
a main text consisting of homogeneous item tokens or vocab tokens that
facilitates stable and effective language modeling. In addition, a novel mutual
regularization strategy is introduced to encourage the CLLM4Rec to capture
recommendation-oriented information from user/item contents. Finally, we
propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where
an item prediction head with multinomial likelihood is added to the pretrained
CLLM4Rec backbone to predict hold-out items based on the soft+hard prompts
established from masked user-item interaction history, where recommendations of
multiple items can be generated efficiently.
|
http://arxiv.org/pdf/2311.01343
|
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
|
cs.IR
| null | null |
cs.IR
|
20231102
|
20231108
|
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{
"authors": "Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li",
"chunk_id": 79,
"doc_id": "2311.01343",
"primary_category": "cs.IR",
"published": 20231102,
"source": "http://arxiv.org/pdf/2311.01343",
"summary": "Recently, there is a growing interest in developing next-generation\nrecommender systems (RSs) based on pretrained large language models (LLMs),\nfully utilizing their encoded knowledge and reasoning ability. However, the\nsemantic gap between natural language and recommendation tasks is still not\nwell addressed, leading to multiple issues such as spuriously-correlated\nuser/item descriptors, ineffective language modeling on user/item contents, and\ninefficient recommendations via auto-regression, etc. In this paper, we propose\nCLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and\nID paradigm of RS, aiming to address the above challenges simultaneously. We\nfirst extend the vocabulary of pretrained LLMs with user/item ID tokens to\nfaithfully model the user/item collaborative and content semantics.\nAccordingly, in the pretraining stage, a novel soft+hard prompting strategy is\nproposed to effectively learn user/item collaborative/content token embeddings\nvia language modeling on RS-specific corpora established from user-item\ninteractions and user/item features, where each document is split into a prompt\nconsisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and\na main text consisting of homogeneous item tokens or vocab tokens that\nfacilitates stable and effective language modeling. In addition, a novel mutual\nregularization strategy is introduced to encourage the CLLM4Rec to capture\nrecommendation-oriented information from user/item contents. Finally, we\npropose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where\nan item prediction head with multinomial likelihood is added to the pretrained\nCLLM4Rec backbone to predict hold-out items based on the soft+hard prompts\nestablished from masked user-item interaction history, where recommendations of\nmultiple items can be generated efficiently.",
"text": "B.2.2 Results & Analysis. The experimental results are summa- rized in Table 4. We can find that although CLLM4Rec with T5 back- bone generally outperforms ID-based and shallow LM-based base- lines, its performance is consistently worse than CLLM4Rec with GPT-2 backbone. The overall inferior performance of CLLM4Rec with T5 backbone can be two-fold. First, we note that the vocab embeddings in T5 are initialized with unit variance, whereas embed- dings in GPT-2 are initialized with a variance of 0.02. Therefore, the weights and embeddings in T5 has much larger numerical values, which leads to large update steps when errors are backpropagating from the outputs to the prompts. Therefore, the training is not as stable as the GPT-2 backbone. In addition, in the finetuning stage of the original T5 model, the prompts are generally used to guide the macro behavior of the model. e.g., changing the model behavior from question answering to machine generation via prompt \"trans- late English to French\". Therefore, another reason for the inferiority of T5 backbone could be the mismatch between the original T5 prompts and the prompts intended to be used in CLLM4Rec.\n8https://huggingface.co/t5-base.",
"title": "Collaborative Large Language Model for Recommender Systems",
"year": 2023
}
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] |
2310.12397
| 1 |
There has been considerable divergence of opinion on the reasoning abilities of Large Language Models (LLMs). While the initial optimism that reasoning might emerge automatically with scale has been tempered thanks to a slew of counterexamplesâranging from multiplication to simple planning, there is still the wide spread belief that LLMs can self-critique and improve their own solutions in an iterative fashion. This belief seemingly rests on the assumption that verification of correctness should be easier than generationâa rather classical argument from computational complexity, that should be irrelevant to LLMs to the extent what they are doing is approximate retrieval. In this paper, we set out to systematically investigate the effectiveness of iterative prompting of LLMs in the context of Graph Coloring, a canonical NP-complete reasoning problem that is related to proposi- tional satisfiability as well as practical problems like scheduling and allocation. We present a principled empirical study of the performance of GPT4 in solving graph coloring instances or verifying the correctness of candidate coloringsâboth in direct and iterative modes. In iterative modes, we experiment both with the model critiquing its own answers and an external correct reasoner verifying
|
2310.12397#1
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 1,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "There has been considerable divergence of opinion on the reasoning abilities of Large Language Models (LLMs). While the initial optimism that reasoning might emerge automatically with scale has been tempered thanks to a slew of counterexamplesâranging from multiplication to simple planning, there is still the wide spread belief that LLMs can self-critique and improve their own solutions in an iterative fashion. This belief seemingly rests on the assumption that verification of correctness should be easier than generationâa rather classical argument from computational complexity, that should be irrelevant to LLMs to the extent what they are doing is approximate retrieval. In this paper, we set out to systematically investigate the effectiveness of iterative prompting of LLMs in the context of Graph Coloring, a canonical NP-complete reasoning problem that is related to proposi- tional satisfiability as well as practical problems like scheduling and allocation. We present a principled empirical study of the performance of GPT4 in solving graph coloring instances or verifying the correctness of candidate coloringsâboth in direct and iterative modes. In iterative modes, we experiment both with the model critiquing its own answers and an external correct reasoner verifying",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 1 |
With the development of large language models (LLMs), striking a balance be- tween the performance and safety of AI systems has never been more critical. However, the inherent tension between the objectives of helpfulness and harmless- ness presents a significant challenge during LLM training. To address this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly decouples human preferences regarding helpfulness and harmlessness, effectively avoiding the crowdworkersâ confusion about the tension and allowing us to train separate reward and cost models. We formalize the safety concern of LLMs as an opti- mization task of maximizing the reward function while satisfying specified cost constraints. Leveraging the Lagrangian method to solve this constrained problem, Safe RLHF dynamically adjusts the balance between the two objectives during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we demonstrate a superior ability to mitigate harmful responses while enhancing model perfor- mance compared to existing value-aligned algorithms. Experimentally, we fine- tuned the Alpaca-7B using Safe RLHF and aligned it with collected human pref- erences, significantly improving its helpfulness and harmlessness according to hu- man evaluations. Code is available at https://github.com/PKU-Alignment/safe-rlhf. Warning: This paper contains example data that may be offensive or harmful.
|
2310.12773#1
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 1,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "With the development of large language models (LLMs), striking a balance be- tween the performance and safety of AI systems has never been more critical. However, the inherent tension between the objectives of helpfulness and harmless- ness presents a significant challenge during LLM training. To address this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly decouples human preferences regarding helpfulness and harmlessness, effectively avoiding the crowdworkersâ confusion about the tension and allowing us to train separate reward and cost models. We formalize the safety concern of LLMs as an opti- mization task of maximizing the reward function while satisfying specified cost constraints. Leveraging the Lagrangian method to solve this constrained problem, Safe RLHF dynamically adjusts the balance between the two objectives during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we demonstrate a superior ability to mitigate harmful responses while enhancing model perfor- mance compared to existing value-aligned algorithms. Experimentally, we fine- tuned the Alpaca-7B using Safe RLHF and aligned it with collected human pref- erences, significantly improving its helpfulness and harmlessness according to hu- man evaluations. Code is available at https://github.com/PKU-Alignment/safe-rlhf. Warning: This paper contains example data that may be offensive or harmful.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 2 |
in direct and iterative modes. In iterative modes, we experiment both with the model critiquing its own answers and an external correct reasoner verifying proposed solutions. In both cases, we analyze whether the content of the criticisms actually affects bottom line performance. The study seems to indicate that (i) LLMs are bad at solving graph coloring instances (ii) they are no better at verifying a solutionâand thus are not effective in iterative modes with LLMs critiquing LLM-generated solutions (iii) the correctness and content of the criticismsâwhether by LLMs or external solversâseems largely irrelevant to the performance of iterative prompting. We show that the observed effectiveness of LLMs in iterative settings is largely due to the correct solution being fortuitously present in the top-k completions of the prompt (and being recognized as such by an external verifier). Our results thus call into question claims about the self-critiquing capabilities of state of the art LLMs.
|
2310.12397#2
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 2,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "in direct and iterative modes. In iterative modes, we experiment both with the model critiquing its own answers and an external correct reasoner verifying proposed solutions. In both cases, we analyze whether the content of the criticisms actually affects bottom line performance. The study seems to indicate that (i) LLMs are bad at solving graph coloring instances (ii) they are no better at verifying a solutionâand thus are not effective in iterative modes with LLMs critiquing LLM-generated solutions (iii) the correctness and content of the criticismsâwhether by LLMs or external solversâseems largely irrelevant to the performance of iterative prompting. We show that the observed effectiveness of LLMs in iterative settings is largely due to the correct solution being fortuitously present in the top-k completions of the prompt (and being recognized as such by an external verifier). Our results thus call into question claims about the self-critiquing capabilities of state of the art LLMs.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 2 |
# INTRODUCTION
Large Language Models (LLMs) have shown remarkable capabilities in understanding instruc- tions (Chung et al., 2022; Ouyang et al., 2022), summarization (Stiennon et al., 2020; Koh et al., 2022) and performing complex reasoning tasks (OpenAI, 2023; Anil et al., 2023), and more. Con- currently, AI systems that leverage LLMs are increasingly enhancing the efficiency of numerous human activities, such as coding (Chen et al., 2021; Gao et al., 2023), medical assistance (Yang et al., 2022; Moor et al., 2023), education (Kasneci et al., 2023; Kung et al., 2023), law (Katz et al., 2023), and so forth. Considering the potential for broad societal impact, responses generated by LLMs must not contain harmful content, such as discrimination, misinformation, or violations of social norms and morals (Gehman et al., 2020; Weidinger et al., 2021; Ganguli et al., 2022; Desh- pande et al., 2023). Therefore, the alignment of safety in LLMs has received widespread attention from academia and industry (Christian, 2023).
An essential component of safety alignment involves minimizing the tendency of a model to generate harmful responses through fine-tuning. Recent works demonstrate that Reinforcement Learning
# âEqual Contribution.
1
|
2310.12773#2
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 2,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "# INTRODUCTION\nLarge Language Models (LLMs) have shown remarkable capabilities in understanding instruc- tions (Chung et al., 2022; Ouyang et al., 2022), summarization (Stiennon et al., 2020; Koh et al., 2022) and performing complex reasoning tasks (OpenAI, 2023; Anil et al., 2023), and more. Con- currently, AI systems that leverage LLMs are increasingly enhancing the efficiency of numerous human activities, such as coding (Chen et al., 2021; Gao et al., 2023), medical assistance (Yang et al., 2022; Moor et al., 2023), education (Kasneci et al., 2023; Kung et al., 2023), law (Katz et al., 2023), and so forth. Considering the potential for broad societal impact, responses generated by LLMs must not contain harmful content, such as discrimination, misinformation, or violations of social norms and morals (Gehman et al., 2020; Weidinger et al., 2021; Ganguli et al., 2022; Desh- pande et al., 2023). Therefore, the alignment of safety in LLMs has received widespread attention from academia and industry (Christian, 2023).\nAn essential component of safety alignment involves minimizing the tendency of a model to generate harmful responses through fine-tuning. Recent works demonstrate that Reinforcement Learning\n# âEqual Contribution.\n1",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 3 |
# Introduction
Large Language Models (LLMs), essentially n-gram models on steroids which have been trained on web-scale language corpus, have caught the imagination of the AI research community with linguistic behaviors that no one expected text completion systems to possess. Their seeming versatility has lead many researchers to wonder whether they can also do well on reasoning tasks typically associated with system 2 competency. Initial excitement based on anecdotal performance of LLMs on reasoning tasks has dissipated to some extent by the recent spate of studies questioning the robustness of such behaviorsâbe it planning [17, 8], simple arithmetic and logic [5], or general mathematical and abstract benchmark[14, 6]. There still exists considerable optimism that even if LLMs canât generate correct solutions in one go, their accuracy improves in a iterative prompting regime, where LLMs will be able to "self-critique" their candidate solutions and refine them to the point of correctness [20, 19, 15, 18, 7]. This belief seem to rest largely on the assumption that verification of correctness
# âArizona State University, Tempe.
Preprint. Under review.
should be easier than generation for many reasoning problemsâa rather classical argument from computational complexity. There are grounds to be skeptical of this assumption as complexity of the reasoning task should be irrelevant to LLM performance if what they are doing is approximate retrieval.
|
2310.12397#3
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 3,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "# Introduction\nLarge Language Models (LLMs), essentially n-gram models on steroids which have been trained on web-scale language corpus, have caught the imagination of the AI research community with linguistic behaviors that no one expected text completion systems to possess. Their seeming versatility has lead many researchers to wonder whether they can also do well on reasoning tasks typically associated with system 2 competency. Initial excitement based on anecdotal performance of LLMs on reasoning tasks has dissipated to some extent by the recent spate of studies questioning the robustness of such behaviorsâbe it planning [17, 8], simple arithmetic and logic [5], or general mathematical and abstract benchmark[14, 6]. There still exists considerable optimism that even if LLMs canât generate correct solutions in one go, their accuracy improves in a iterative prompting regime, where LLMs will be able to \"self-critique\" their candidate solutions and refine them to the point of correctness [20, 19, 15, 18, 7]. This belief seem to rest largely on the assumption that verification of correctness\n# âArizona State University, Tempe.\nPreprint. Under review.\nshould be easier than generation for many reasoning problemsâa rather classical argument from computational complexity. There are grounds to be skeptical of this assumption as complexity of the reasoning task should be irrelevant to LLM performance if what they are doing is approximate retrieval.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 3 |
\ >) Supervised Fine-tuning Data Collection Preference Annotation & Preference Modeling Policy Optimization BE Human Annotators Safe RLHE BEGINNING OF CONVERSATION: CESREINSCICONETEAS TEND Reward Model Rg(y, x) Reward Objective you help me figure out hhow to be a serial killer? USER: Give three tips for staying healthy. ASSISTANT: om a | Previous Cmaeea 1. Eat a balanced diet and make sure AS Stage t - Je(®) = By-xgc[CyO.x)] +d vegetables. H eee Se oepE » \e Optimization Objective cena =) 3. Get enough sleep and maintain a Biepenro ds aetna as Unity, no, Wed an poe advice on how to become a serial killer or engage in any illegal a activities Reward Objective Pretrain Model âSFT Model Ie (0) = By-ngc polo] ( ) âBecoming a serial killer requires '_» ' Ge Opetne careful planning, patience, and above : ' |S Optimization Objective all, a strong menial tate » UN} VS Firstly, you need ' / : 7 r +) IR(O) = Ey-noc [Ro O29] uâ_Y
|
2310.12773#3
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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] |
{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 3,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "\\ >) Supervised Fine-tuning Data Collection Preference Annotation & Preference Modeling Policy Optimization BE Human Annotators Safe RLHE BEGINNING OF CONVERSATION: CESREINSCICONETEAS TEND Reward Model Rg(y, x) Reward Objective you help me figure out hhow to be a serial killer? USER: Give three tips for staying healthy. ASSISTANT: om a | Previous Cmaeea 1. Eat a balanced diet and make sure AS Stage t - Je(®) = By-xgc[CyO.x)] +d vegetables. H eee Se oepE » \\e Optimization Objective cena =) 3. Get enough sleep and maintain a Biepenro ds aetna as Unity, no, Wed an poe advice on how to become a serial killer or engage in any illegal a activities Reward Objective Pretrain Model âSFT Model Ie (0) = By-ngc polo] ( ) âBecoming a serial killer requires '_» ' Ge Opetne careful planning, patience, and above : ' |S Optimization Objective all, a strong menial tate » UN} VS Firstly, you need ' / : 7 r +) IR(O) = Ey-noc [Ro O29] uâ_Y",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 4 |
In this paper, we set out to systematically investigate effectiveness of iterative prompting in the context of Graph Coloring, a canonical NP-complete reasoning problem. We chose graph coloring as it is representative both of standard classes of reasoning problems studied in AIâpropositional satisfiability and constraint satisfactionâand practical problems like scheduling and allocation. Our methodology involves a principled empirical study of the performance of GPT4 on two tasks: solving a large suite of random graph coloring instances and, separately, verifying the correctness of the candidate coloringsâboth in direct and iterative modes. In iterative modes, we experiment both with an LLM critiquing LLM-produced solutions and an external, guaranteed correct reasoner verifying solutions. In both cases, we analyze whether the content of criticisms actually affects bottom line performance.
Our results indicate that in direct mode, LLMs are, perhaps not surprisingly, pretty bad at solving graph coloring instances. More interestingly, as we suspected, they are no better at verifying solutions. In iterative modes, given the inability of LLMs to verify solutions, it should come as no surprise that our experiments show that the strategy of LLMs self-critiquing their solutions does not improve over the baseline. It is actually worse because the system canât recognize a correct coloring and thus merrily passes over fortuitously correct colorings it has generated, ending up with a wrong one!
|
2310.12397#4
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 4,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "In this paper, we set out to systematically investigate effectiveness of iterative prompting in the context of Graph Coloring, a canonical NP-complete reasoning problem. We chose graph coloring as it is representative both of standard classes of reasoning problems studied in AIâpropositional satisfiability and constraint satisfactionâand practical problems like scheduling and allocation. Our methodology involves a principled empirical study of the performance of GPT4 on two tasks: solving a large suite of random graph coloring instances and, separately, verifying the correctness of the candidate coloringsâboth in direct and iterative modes. In iterative modes, we experiment both with an LLM critiquing LLM-produced solutions and an external, guaranteed correct reasoner verifying solutions. In both cases, we analyze whether the content of criticisms actually affects bottom line performance.\nOur results indicate that in direct mode, LLMs are, perhaps not surprisingly, pretty bad at solving graph coloring instances. More interestingly, as we suspected, they are no better at verifying solutions. In iterative modes, given the inability of LLMs to verify solutions, it should come as no surprise that our experiments show that the strategy of LLMs self-critiquing their solutions does not improve over the baseline. It is actually worse because the system canât recognize a correct coloring and thus merrily passes over fortuitously correct colorings it has generated, ending up with a wrong one!",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12397
| 5 |
We next experimented with an iterative strategy where an external coloring verifier does the back- prompting. Here we looked at three different types of back prompting: (1) the verifier just asks the LLM to try again when the coloring is incorrect, (2) the verifier gives a backprompt showing the first violated constraint in the current candidate coloring and (3) the verifier sends a backprompt showing all violated coloring constraints. We note that these three strategies do lead to modest improvements in the bottom-line performanceâimproving from about 16% to nearly 40%. The surprising finding however is that the minimal information "try again" feedback is nearly as effective as the ones with meaningful backprompts. This lead us to consider whether the improvement is due to the type of backprompting (as authors who advocate these types of iterative approaches [20, 19, 15, 10, 4, 11] seem to assume) or because the answer just happens to be in the top-K completions (even if the LLM is itself not cognizant of it). To check this, we experiment with a version of the direct mode where we query the LLM so that it generates more than one potential solution, and have the external verifier pick out any correct solution in the list. The results show that top-k correctness with an external, guaranteed correct verifier is pretty competitive with any iterative backprompting.
|
2310.12397#5
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 5,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "We next experimented with an iterative strategy where an external coloring verifier does the back- prompting. Here we looked at three different types of back prompting: (1) the verifier just asks the LLM to try again when the coloring is incorrect, (2) the verifier gives a backprompt showing the first violated constraint in the current candidate coloring and (3) the verifier sends a backprompt showing all violated coloring constraints. We note that these three strategies do lead to modest improvements in the bottom-line performanceâimproving from about 16% to nearly 40%. The surprising finding however is that the minimal information \"try again\" feedback is nearly as effective as the ones with meaningful backprompts. This lead us to consider whether the improvement is due to the type of backprompting (as authors who advocate these types of iterative approaches [20, 19, 15, 10, 4, 11] seem to assume) or because the answer just happens to be in the top-K completions (even if the LLM is itself not cognizant of it). To check this, we experiment with a version of the direct mode where we query the LLM so that it generates more than one potential solution, and have the external verifier pick out any correct solution in the list. The results show that top-k correctness with an external, guaranteed correct verifier is pretty competitive with any iterative backprompting.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 5 |
Figure 1: Safe RLHF pipeline compared to conventional RLHF method. Our pipeline decouples the data annotation for helpfulness and harmlessness, as well as the training of preference models. Ultimately, it dynamically integrates both aspects during the policy optimization phase. NOTE: In the annotation phase, the safety labels for the responses are annotated independently. These responses can be labeled as both safe or both unsafe.
|
2310.12773#5
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 5,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "Figure 1: Safe RLHF pipeline compared to conventional RLHF method. Our pipeline decouples the data annotation for helpfulness and harmlessness, as well as the training of preference models. Ultimately, it dynamically integrates both aspects during the policy optimization phase. NOTE: In the annotation phase, the safety labels for the responses are annotated independently. These responses can be labeled as both safe or both unsafe.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 6 |
Our investigation thus raises significant grounds to be skeptical about the effectiveness of iterative prompting techniques in general, and those relying on the self-critiquing capabilities of LLMs in particular. In the reminder of the paper, we discuss related work, present our experimental methodology, and then detail the results of our experiments.
# 2 Related Work
As mentioned in the introduction, there has been a large recent body of work investigating the reasoning capabilities of LLMs [15, 19, 9]. The studies span different types of reasoning problemsâ planning [17], logic and arithmetic [5], or 24 puzzle [19]. The conclusions have also been divergentâ with some studies highlighting the limitations of LLMs in reasoning[12, 2], and others arguing that iterative prompting of LLMs can improve their ability to reason. For example, [15] states we explore this emergent property of self-reflection in LLMs and empirically show that self-reflection is extremely useful to learn complex tasks over a handful of trials. This paper focuses on understanding these sorts of claimsâand especially of the effectiveness of iterative prompting. The problem we choseâgraph coloringâis a canonical NP-complete reasoning problem well studied in AI and computer science [13]. It has rich connections to propositional logical reasoningâspecifically satisfiability, constraint satisfaction problems, and is also related to practical problems including resource allocation and scheduling.
2
# 3 Methodology
# 3.1 The Graph Coloring Problem
|
2310.12397#6
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 6,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "Our investigation thus raises significant grounds to be skeptical about the effectiveness of iterative prompting techniques in general, and those relying on the self-critiquing capabilities of LLMs in particular. In the reminder of the paper, we discuss related work, present our experimental methodology, and then detail the results of our experiments.\n# 2 Related Work\nAs mentioned in the introduction, there has been a large recent body of work investigating the reasoning capabilities of LLMs [15, 19, 9]. The studies span different types of reasoning problemsâ planning [17], logic and arithmetic [5], or 24 puzzle [19]. The conclusions have also been divergentâ with some studies highlighting the limitations of LLMs in reasoning[12, 2], and others arguing that iterative prompting of LLMs can improve their ability to reason. For example, [15] states we explore this emergent property of self-reflection in LLMs and empirically show that self-reflection is extremely useful to learn complex tasks over a handful of trials. This paper focuses on understanding these sorts of claimsâand especially of the effectiveness of iterative prompting. The problem we choseâgraph coloringâis a canonical NP-complete reasoning problem well studied in AI and computer science [13]. It has rich connections to propositional logical reasoningâspecifically satisfiability, constraint satisfaction problems, and is also related to practical problems including resource allocation and scheduling.\n2\n# 3 Methodology\n# 3.1 The Graph Coloring Problem",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 6 |
with Human Feedback (RLHF) (Christiano et al., 2017; Ouyang et al., 2022) is a practical approach for aligning LLMs with human preferences, both in terms of style and ethical values (Bai et al., 2022a; Ganguli et al., 2022). RLHF leverages LLMsâ broad knowledge and capabilities to promote desired responses and behaviors, which leads to safer, higher-performing, and more controllable AI systems. Both technical reports from GPT-4 (OpenAI, 2023) and Anthropic (Ganguli et al., 2022) for their LLMs revealed their use of safety-related prompts, constructed through adversarial probing methods like red-teaming, in the RLHF phase to reduce the potential harm of their model. However, the pursuit of increasing helpfulness and harmlessness may often contradict in practice (Ganguli et al., 2022; Bai et al., 2022a). For example, a model refusing to answer can be considered safe, yet it also renders the response unhelpful in extreme scenarios. Thus, a significant challenge arises in balancing the two objectives during the training phase. Our goal is to develop a large language model that is helpful, safe, and willing to respond.
|
2310.12773#6
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 6,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "with Human Feedback (RLHF) (Christiano et al., 2017; Ouyang et al., 2022) is a practical approach for aligning LLMs with human preferences, both in terms of style and ethical values (Bai et al., 2022a; Ganguli et al., 2022). RLHF leverages LLMsâ broad knowledge and capabilities to promote desired responses and behaviors, which leads to safer, higher-performing, and more controllable AI systems. Both technical reports from GPT-4 (OpenAI, 2023) and Anthropic (Ganguli et al., 2022) for their LLMs revealed their use of safety-related prompts, constructed through adversarial probing methods like red-teaming, in the RLHF phase to reduce the potential harm of their model. However, the pursuit of increasing helpfulness and harmlessness may often contradict in practice (Ganguli et al., 2022; Bai et al., 2022a). For example, a model refusing to answer can be considered safe, yet it also renders the response unhelpful in extreme scenarios. Thus, a significant challenge arises in balancing the two objectives during the training phase. Our goal is to develop a large language model that is helpful, safe, and willing to respond.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 7 |
2
# 3 Methodology
# 3.1 The Graph Coloring Problem
Because we are interested in LLMsâ self-critique capabilities, we chose Graph Coloring, a reasoning domain which is human readable, provides relatively short description and critique lengths, and, most importantly, is very easy to verify and provide feedback for. Though it is difficult to be certain, we also believe that this domain is diverse enough even at low node and edge counts that the instances we examine are very unlikely to be found in the LLMâs training data, thus minimizing the risk of model contamination and memorization.
Graph coloring is a a canonical NP-complete reasoning problem that is related to both propositional satisfiability as well as practical problems like scheduling and allocation. It is broad enough to give insights into reasoning more generally, and simple enough to be specified and evaluated by a human or basic pattern matching.
|
2310.12397#7
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 7,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "2\n# 3 Methodology\n# 3.1 The Graph Coloring Problem\nBecause we are interested in LLMsâ self-critique capabilities, we chose Graph Coloring, a reasoning domain which is human readable, provides relatively short description and critique lengths, and, most importantly, is very easy to verify and provide feedback for. Though it is difficult to be certain, we also believe that this domain is diverse enough even at low node and edge counts that the instances we examine are very unlikely to be found in the LLMâs training data, thus minimizing the risk of model contamination and memorization.\nGraph coloring is a a canonical NP-complete reasoning problem that is related to both propositional satisfiability as well as practical problems like scheduling and allocation. It is broad enough to give insights into reasoning more generally, and simple enough to be specified and evaluated by a human or basic pattern matching.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 7 |
To address the above challenge, we propose a novel framework: Safe Reinforcement Learning from Human Feedback (Safe RLHF). The core insight of Safe RLHF is the decoupling of human prefer- ences during data annotation and the establishment of two optimization objectives: helpfulness and harmlessness (as shown in equation (9)). Safe RLHF formalizes the goal of developing harmless LLMs as a constraint under the Safe RL framework. It is crucial that we need a balance between helpfulness and harmlessness objectives, and avoid over-optimizing for harmlessness.
# The decoupling of preferences and objectives offers two advantages:
⢠During the data annotation, it ensures that the feedback from crowdworkers remains unbiased by any tension between helpfulness and harmlessness.
⢠During the Safe RLHF stage, the Lagrangian method (Bertsekas, 1997) can adaptively balance the trade-off between two inherently conflicting training objectives.
|
2310.12773#7
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 7,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "To address the above challenge, we propose a novel framework: Safe Reinforcement Learning from Human Feedback (Safe RLHF). The core insight of Safe RLHF is the decoupling of human prefer- ences during data annotation and the establishment of two optimization objectives: helpfulness and harmlessness (as shown in equation (9)). Safe RLHF formalizes the goal of developing harmless LLMs as a constraint under the Safe RL framework. It is crucial that we need a balance between helpfulness and harmlessness objectives, and avoid over-optimizing for harmlessness.\n# The decoupling of preferences and objectives offers two advantages:\n⢠During the data annotation, it ensures that the feedback from crowdworkers remains unbiased by any tension between helpfulness and harmlessness.\n⢠During the Safe RLHF stage, the Lagrangian method (Bertsekas, 1997) can adaptively balance the trade-off between two inherently conflicting training objectives.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 8 |
Common graph coloring benchmark sets consist of the sorts of problems that exact solvers struggle on, boasting triple or quadruple digit numbers of nodes and edges[16]. Current language models donât have sufficiently large context windows to process these, andâas weâll see laterâare unlikely to do well on graphs with over twenty nodes. Therefore, we built our own dataset. We use GrinPy2 to handle common graph operations. Each graph is constructed using the ErdËosâRényi method (p = 0.4), modified so that any generation that fails to be planar or happens to be isomorphic to a previously generated one is retried. Once a successful candidate is found, it is compiled into the standard DIMACS format[1], appended with a comment containing its precalculated chromatic number.
For the following experiments, we generated 100 instances with an average of 24 edges each spread across node counts from 10 to 17âa distribution chosen because empirical probing revealed it to be an area with volatile enough performance to be interesting. An example of one of the graphs we used is shown in Figure 1, together with the LLMâs first response, the backprompt on that response, and the final correct coloring.
# 3.2 Architecture for Iterative Backprompting
All code and results will be made public.
# Prompt Generator:
|
2310.12397#8
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 8,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "Common graph coloring benchmark sets consist of the sorts of problems that exact solvers struggle on, boasting triple or quadruple digit numbers of nodes and edges[16]. Current language models donât have sufficiently large context windows to process these, andâas weâll see laterâare unlikely to do well on graphs with over twenty nodes. Therefore, we built our own dataset. We use GrinPy2 to handle common graph operations. Each graph is constructed using the ErdËosâRényi method (p = 0.4), modified so that any generation that fails to be planar or happens to be isomorphic to a previously generated one is retried. Once a successful candidate is found, it is compiled into the standard DIMACS format[1], appended with a comment containing its precalculated chromatic number.\nFor the following experiments, we generated 100 instances with an average of 24 edges each spread across node counts from 10 to 17âa distribution chosen because empirical probing revealed it to be an area with volatile enough performance to be interesting. An example of one of the graphs we used is shown in Figure 1, together with the LLMâs first response, the backprompt on that response, and the final correct coloring.\n# 3.2 Architecture for Iterative Backprompting\nAll code and results will be made public.\n# Prompt Generator:",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 8 |
⢠During the Safe RLHF stage, the Lagrangian method (Bertsekas, 1997) can adaptively balance the trade-off between two inherently conflicting training objectives.
To the best of our knowledge, Safe RLHF is the first integration of Safe RL and the RLHF frame- work. This framework incorporates a two-dimensional human annotation scheme and a safe training mechanism to enhance model performance while ensuring safety (as shown in Figure 1). Experi- mentally, we applied the Safe RLHF pipeline three times, significantly enhancing the helpfulness of the base SFT model while efficiently reducing the generation of harmful responses. Compared to the static multi-objective balance algorithm, Reward Shaping (Ng et al., 1999), Our algorithm bet- ter navigates the tension between the objectives of helpfulness and harmlessness. Simultaneously, it maintains equal or superior performance improvements compared to existing value-aligned algo- rithms. Meanwhile, we release all the data and training codes from the three iterations of Safe RLHF fine-tuning, facilitating researchers to replicate and validate our findings.
2
# 2 PRELIMINARIES
|
2310.12773#8
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2302.13971"
},
{
"id": "2304.05302"
},
{
"id": "1707.06347"
},
{
"id": "2305.18290"
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{
"id": "2305.10403"
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{
"id": "2009.11462"
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{
"id": "2112.00861"
},
{
"id": "2209.14375"
},
{
"id": "2303.00957"
},
{
"id": "2306.01693"
},
{
"id": "2305.01937"
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{
"id": "2307.15217"
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{
"id": "2306.17492"
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{
"id": "2112.04359"
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{
"id": "1506.02438"
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] |
{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 8,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "⢠During the Safe RLHF stage, the Lagrangian method (Bertsekas, 1997) can adaptively balance the trade-off between two inherently conflicting training objectives.\nTo the best of our knowledge, Safe RLHF is the first integration of Safe RL and the RLHF frame- work. This framework incorporates a two-dimensional human annotation scheme and a safe training mechanism to enhance model performance while ensuring safety (as shown in Figure 1). Experi- mentally, we applied the Safe RLHF pipeline three times, significantly enhancing the helpfulness of the base SFT model while efficiently reducing the generation of harmful responses. Compared to the static multi-objective balance algorithm, Reward Shaping (Ng et al., 1999), Our algorithm bet- ter navigates the tension between the objectives of helpfulness and harmlessness. Simultaneously, it maintains equal or superior performance improvements compared to existing value-aligned algo- rithms. Meanwhile, we release all the data and training codes from the three iterations of Safe RLHF fine-tuning, facilitating researchers to replicate and validate our findings.\n2\n# 2 PRELIMINARIES",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 9 |
# 3.2 Architecture for Iterative Backprompting
All code and results will be made public.
# Prompt Generator:
The generator takes a DIMACS instance and constructs a natural language prompt by translating each edge into a sentence and then wrapping the whole in a common set of instructions. We deliberately minimize differences between instancesâ prompts to reduce how much problem-specific information we leak to the LLM. Examples of each prompt type can be found in the appendix.
# Large Language Model:
Off the shelf, this system allows for the use of any LLM accessible through the OpenAI API: the user need only pass the model name through the appropriate flag at runtime. The present work focuses on GPT-4, the current state of the art, because of recent claims about its "emergent" reasoning capabilities[3].
We provide a system role of "You are a constraint satisfaction solver that solves various CSP problems." and set the temperature to 0, thus ensuring output is mostly deterministic.
# Extensibility:
This architecture easily extends to other domains of constraint satisfaction problem solving. In the public repository, we provide a way to add a new domain description by adding just one file to the project in plug-and-play fashion.
# 2https://pypi.org/project/grinpy/
3
propose backprompt candidate if solution incorrect Generator when correct or Sound Coloring feedback limit Verifier exceeded fe
|
2310.12397#9
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 9,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "# 3.2 Architecture for Iterative Backprompting\nAll code and results will be made public.\n# Prompt Generator:\nThe generator takes a DIMACS instance and constructs a natural language prompt by translating each edge into a sentence and then wrapping the whole in a common set of instructions. We deliberately minimize differences between instancesâ prompts to reduce how much problem-specific information we leak to the LLM. Examples of each prompt type can be found in the appendix.\n# Large Language Model:\nOff the shelf, this system allows for the use of any LLM accessible through the OpenAI API: the user need only pass the model name through the appropriate flag at runtime. The present work focuses on GPT-4, the current state of the art, because of recent claims about its \"emergent\" reasoning capabilities[3].\nWe provide a system role of \"You are a constraint satisfaction solver that solves various CSP problems.\" and set the temperature to 0, thus ensuring output is mostly deterministic.\n# Extensibility:\nThis architecture easily extends to other domains of constraint satisfaction problem solving. In the public repository, we provide a way to add a new domain description by adding just one file to the project in plug-and-play fashion.\n# 2https://pypi.org/project/grinpy/\n3\npropose backprompt candidate if solution incorrect Generator when correct or Sound Coloring feedback limit Verifier exceeded fe",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 9 |
2
# 2 PRELIMINARIES
Preference Modelling The RLHF method enhances the quality of language model responses by leveraging human preference data through a reward model. The reward model is denoted as RÏ(y, x), where x is the input prompt, y is the response generated by the language model, and R is the scalar output from the reward model. Human preference data is symbolized as yw â» yl|x, where yw (win) denotes a response that is more preferred by humans compared to yl (lose). Most of the previous work, including Christiano et al. (2017); Sadigh et al. (2017); Bai et al. (2022a); Kim et al. (2023), employs a preference predictor adhering to the Bradley-Terry model (Bradley & Terry, 1952). The likelihood of a preference pair can be estimated as:
pâ(yw â» yl|x) = exp(R(yw, x)) exp(R(yw, x)) + exp(R(yl, x)) = Ï(R(yw, x) â R(yl, x)), (1)
|
2310.12773#9
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 9,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "2\n# 2 PRELIMINARIES\nPreference Modelling The RLHF method enhances the quality of language model responses by leveraging human preference data through a reward model. The reward model is denoted as RÏ(y, x), where x is the input prompt, y is the response generated by the language model, and R is the scalar output from the reward model. Human preference data is symbolized as yw â» yl|x, where yw (win) denotes a response that is more preferred by humans compared to yl (lose). Most of the previous work, including Christiano et al. (2017); Sadigh et al. (2017); Bai et al. (2022a); Kim et al. (2023), employs a preference predictor adhering to the Bradley-Terry model (Bradley & Terry, 1952). The likelihood of a preference pair can be estimated as:\npâ(yw â» yl|x) = exp(R(yw, x)) exp(R(yw, x)) + exp(R(yl, x)) = Ï(R(yw, x) â R(yl, x)), (1)",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 10 |
# 2https://pypi.org/project/grinpy/
3
propose backprompt candidate if solution incorrect Generator when correct or Sound Coloring feedback limit Verifier exceeded fe
Figure 1: Overview of backprompt architecture for a single instance. Clouds provide an illustrated interpretation of the current state of the problem at different points in the system. Red diamonds indicate progression of a single problem: a planar graph is first passed to GPT-4 acting as a generator (1), which returns a proposed coloring (2). GPT-4 will then be used as a verifier to determine whether the coloring is correct. When not correct, GPT-4 provides feedback, along with previous history, through a backprompt (3) that will be used in the next generation request (4). Each new coloring will be evaluated by the GPT-4 working as a verifier. If GPT-4 determines the coloring to be correct or 15 iterations have passed, it approves the final answer, where it is then evaluated against a sound verifier.
# 3.3 Backprompt Generation
In verification mode, the LLM receives a different sort of prompt. Apart from standard instructions, it contains only the graph description and the proposed coloring. It is tasked with verifying correctness, optimality, and whether every vertex has been given an assignment. If the coloring is incorrect, it must reply with a set of contradicting edges.
|
2310.12397#10
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 10,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "# 2https://pypi.org/project/grinpy/\n3\npropose backprompt candidate if solution incorrect Generator when correct or Sound Coloring feedback limit Verifier exceeded fe\nFigure 1: Overview of backprompt architecture for a single instance. Clouds provide an illustrated interpretation of the current state of the problem at different points in the system. Red diamonds indicate progression of a single problem: a planar graph is first passed to GPT-4 acting as a generator (1), which returns a proposed coloring (2). GPT-4 will then be used as a verifier to determine whether the coloring is correct. When not correct, GPT-4 provides feedback, along with previous history, through a backprompt (3) that will be used in the next generation request (4). Each new coloring will be evaluated by the GPT-4 working as a verifier. If GPT-4 determines the coloring to be correct or 15 iterations have passed, it approves the final answer, where it is then evaluated against a sound verifier.\n# 3.3 Backprompt Generation\nIn verification mode, the LLM receives a different sort of prompt. Apart from standard instructions, it contains only the graph description and the proposed coloring. It is tasked with verifying correctness, optimality, and whether every vertex has been given an assignment. If the coloring is incorrect, it must reply with a set of contradicting edges.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12397
| 11 |
As a comparison point, we also construct a guaranteed correct verifier, with the ability to list every single contradicting edge. Since LLM responses are also in natural language, we first translate them into a format amenable to analysis. To make this process more consistent, we design our initial prompt to describe an exact output format to which the model conforms. Then, the response is evaluated for correctness.
In both cases, if the verifier says the answer is correct, we end there. If it has been more than 15 rounds (16 total queries), we give up. Otherwise, a backprompt is created, wrapped in standard instructions, appended to the previous message history, and sent back to the model as a new prompt.
In this domain a valid piece of error feedback consists of a pair of vertices which were given the same color but share an edge. To construct a backprompt, we have to decide exactly how much feedback to give. We examine five cases:
1. None: A single iteration baseline. No backprompting.
2. Pass/Fail: The only feedback given is that the answer was incorrect.
# nk WN
3. First: Only the first error encountered is returned.
4. Full: A comprehensive list of errors.
5. LLM: Feedback is provided by the language model through a separate prompt, given in the appendix. We pass any and all response back to the generator, regardless of its validity or correctness.
|
2310.12397#11
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 11,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "As a comparison point, we also construct a guaranteed correct verifier, with the ability to list every single contradicting edge. Since LLM responses are also in natural language, we first translate them into a format amenable to analysis. To make this process more consistent, we design our initial prompt to describe an exact output format to which the model conforms. Then, the response is evaluated for correctness.\nIn both cases, if the verifier says the answer is correct, we end there. If it has been more than 15 rounds (16 total queries), we give up. Otherwise, a backprompt is created, wrapped in standard instructions, appended to the previous message history, and sent back to the model as a new prompt.\nIn this domain a valid piece of error feedback consists of a pair of vertices which were given the same color but share an edge. To construct a backprompt, we have to decide exactly how much feedback to give. We examine five cases:\n1. None: A single iteration baseline. No backprompting.\n2. Pass/Fail: The only feedback given is that the answer was incorrect.\n# nk WN\n3. First: Only the first error encountered is returned.\n4. Full: A comprehensive list of errors.\n5. LLM: Feedback is provided by the language model through a separate prompt, given in the appendix. We pass any and all response back to the generator, regardless of its validity or correctness.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 11 |
Safe Reinforcement Learning A Markov Decision Process (MDP) (Puterman, 2014), M 4 (S,A,r,P, Wo, 7), including the state space S, the action space A, a reward function r, the tran- sition probability P, the initial state distribution fio, and a discount factor 7. In this framework, a stationary policy, 7, is a probability distribution indicating the likelihood of taking action a in state s. The state value function V"(s) = E,.7 [Sop y'rt | 80 = 8] denotes the expected cumulative discounted reward over time, starting from s. Then, the primary objective of reinforcement learning is to maximize the objective function, 7 (79) = Es.<yo [Viz (So)]- Generally, Safe RL is formulated as a Constrained MDP (CMDP) M UC (Altman, 2021), which extends the standard MDP JM with an additional constraint set C. The set C = {(ci,bi)}i, is composed of cost functions c; and cost thresholds b;,i=1,...,m. The cost return is defined as J (79) = Eny [cpio yc: (s141|8t,@t)], and the
|
2310.12773#11
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"id": "2210.11416"
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{
"id": "2212.08073"
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"id": "2009.11462"
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"id": "2307.04657"
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"id": "2204.05862"
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"id": "2112.00861"
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"id": "2209.14375"
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"id": "2303.00957"
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"id": "2306.01693"
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] |
{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 11,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "Safe Reinforcement Learning A Markov Decision Process (MDP) (Puterman, 2014), M 4 (S,A,r,P, Wo, 7), including the state space S, the action space A, a reward function r, the tran- sition probability P, the initial state distribution fio, and a discount factor 7. In this framework, a stationary policy, 7, is a probability distribution indicating the likelihood of taking action a in state s. The state value function V\"(s) = E,.7 [Sop y'rt | 80 = 8] denotes the expected cumulative discounted reward over time, starting from s. Then, the primary objective of reinforcement learning is to maximize the objective function, 7 (79) = Es.<yo [Viz (So)]- Generally, Safe RL is formulated as a Constrained MDP (CMDP) M UC (Altman, 2021), which extends the standard MDP JM with an additional constraint set C. The set C = {(ci,bi)}i, is composed of cost functions c; and cost thresholds b;,i=1,...,m. The cost return is defined as J (79) = Eny [cpio yc: (s141|8t,@t)], and the",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 12 |
5. LLM: Feedback is provided by the language model through a separate prompt, given in the appendix. We pass any and all response back to the generator, regardless of its validity or correctness.
By comparing results under these regimes, we can deduce how much of the given information the LLM is actually using, versus how much of the performance increase stems from merely getting
4
more tries. We also compare these cases to four further cases: higher temperature, single iteration queries which ask for multiple answers. These do not involve any backprompting, reprompting, or giving any information past the original prompt to the LLM.
6-8. Top 5: With temperatures 0.5, 1, and 1.5, query the LLM for n = 5 responses.
9. Top 15: With a temperature of 1, query the LLM for n = 15 responses.
# 3.4 Verification
In order to gain more insight into their LLM verification, we examine how well they find errors in proposed colorings. Intuitively, these should be very easy to identify: if the two vertices making up an edge share a color, immediately return that edge. Algorithmically, all this requires is looping over edges and comparing each vertexâs color to that of its partner.
|
2310.12397#12
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 12,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "5. LLM: Feedback is provided by the language model through a separate prompt, given in the appendix. We pass any and all response back to the generator, regardless of its validity or correctness.\nBy comparing results under these regimes, we can deduce how much of the given information the LLM is actually using, versus how much of the performance increase stems from merely getting\n4\nmore tries. We also compare these cases to four further cases: higher temperature, single iteration queries which ask for multiple answers. These do not involve any backprompting, reprompting, or giving any information past the original prompt to the LLM.\n6-8. Top 5: With temperatures 0.5, 1, and 1.5, query the LLM for n = 5 responses.\n9. Top 15: With a temperature of 1, query the LLM for n = 15 responses.\n# 3.4 Verification\nIn order to gain more insight into their LLM verification, we examine how well they find errors in proposed colorings. Intuitively, these should be very easy to identify: if the two vertices making up an edge share a color, immediately return that edge. Algorithmically, all this requires is looping over edges and comparing each vertexâs color to that of its partner.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12397
| 13 |
We use the same pipeline for this analysis, but construct a new domain we call color_verification. The LLM is prompted to check correctness, optimality, and if every vertex has been assigned in the coloring. If the coloring is incorrect, it is instructed to list errors in the coloring, that is, if two connected nodes share a color, it is to return the edge to represent the error. No backprompts are given. We use the same graph instances from before, but generate four kinds of colorings to test the model on:
1. Correct: Optimal colorings with no errors, generated via iterated, randomized greedy algorithm (with a precomputed chromatic number to ensure optimality)
2. Ablated: The previous set of colorings, each with a random node changed to one of its neighborâs colors
3. Non-optimal: The correct set, with a randomly chosen color partially recolored to a new shade
4. Random: Completely randomly assigned colors, with the number of different colors equal to the graphâs chromatic number
5. LLM: Colorings randomly selected from the LLM-generated outputs of the previous experi- ment
# 4 Results
# 4.1 Backprompting as Self-Critique
|
2310.12397#13
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 13,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "We use the same pipeline for this analysis, but construct a new domain we call color_verification. The LLM is prompted to check correctness, optimality, and if every vertex has been assigned in the coloring. If the coloring is incorrect, it is instructed to list errors in the coloring, that is, if two connected nodes share a color, it is to return the edge to represent the error. No backprompts are given. We use the same graph instances from before, but generate four kinds of colorings to test the model on:\n1. Correct: Optimal colorings with no errors, generated via iterated, randomized greedy algorithm (with a precomputed chromatic number to ensure optimality)\n2. Ablated: The previous set of colorings, each with a random node changed to one of its neighborâs colors\n3. Non-optimal: The correct set, with a randomly chosen color partially recolored to a new shade\n4. Random: Completely randomly assigned colors, with the number of different colors equal to the graphâs chromatic number\n5. LLM: Colorings randomly selected from the LLM-generated outputs of the previous experi- ment\n# 4 Results\n# 4.1 Backprompting as Self-Critique",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 13 |
Ïâ = arg max ÏθâÎ C J (Ïθ). (3)
# 3 METHOD: SAFE RLHF
As shown in Figure 1, we introduce our Safe RLHF pipeline, which leverages the Safe RL frame- work to balance the tension between the helpfulness and harmfulness objectives. Compared to the conventional RLHF (Ouyang et al., 2022), Safe RLHF introduces substantial modifications, specif- ically in the stages of Preference Annotation & Modeling and Policy Optimization.
3.1 HUMAN PREFERENCE OF HARMLESSNESS AND HELPFULNESS
|
2310.12773#13
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 13,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "Ïâ = arg max ÏθâÎ C J (Ïθ). (3)\n# 3 METHOD: SAFE RLHF\nAs shown in Figure 1, we introduce our Safe RLHF pipeline, which leverages the Safe RL frame- work to balance the tension between the helpfulness and harmfulness objectives. Compared to the conventional RLHF (Ouyang et al., 2022), Safe RLHF introduces substantial modifications, specif- ically in the stages of Preference Annotation & Modeling and Policy Optimization.\n3.1 HUMAN PREFERENCE OF HARMLESSNESS AND HELPFULNESS",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 14 |
5. LLM: Colorings randomly selected from the LLM-generated outputs of the previous experi- ment
# 4 Results
# 4.1 Backprompting as Self-Critique
Figure 2: Performance versus backprompting tech- nique. Correctness is evaluated for the response the verifier claims as correct, or after 15 iterations. Figure 3: Performance versus sampling technique. An instance is marked correct if any answer in the top n was correct.
_ Performance Across Backprompting Regimes 40% 3 & % 30% H g S 20% 2 é 10% 0% exten verter Exteal verre verter (no eackprompt) SeltCritque Type = oe
_ Performance Across Sampling Regimes. z 2 40% & 8 S 30% 3 g & 20% eS Z 8 10% @ Top 15 op 5 Tp 5 25 ee bs who rhs Number of Samples and Sampling Temperature
Prompting the LLM, evaluating the answer, and moving on to the next instance without any back- prompts whatsoever gives a baseline score of 16%. When we run the same instances, but this time backprompt the LLM with feedback generated by the same language model acting as a verifier, performance plummetsâonly a single instance of the 100 was answered correctly.
5
# Table 1: Summary of Backprompt Techniques Example Prompt
|
2310.12397#14
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 14,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "5. LLM: Colorings randomly selected from the LLM-generated outputs of the previous experi- ment\n# 4 Results\n# 4.1 Backprompting as Self-Critique\nFigure 2: Performance versus backprompting tech- nique. Correctness is evaluated for the response the verifier claims as correct, or after 15 iterations. Figure 3: Performance versus sampling technique. An instance is marked correct if any answer in the top n was correct. \n_ Performance Across Backprompting Regimes 40% 3 & % 30% H g S 20% 2 é 10% 0% exten verter Exteal verre verter (no eackprompt) SeltCritque Type = oe\n_ Performance Across Sampling Regimes. z 2 40% & 8 S 30% 3 g & 20% eS Z 8 10% @ Top 15 op 5 Tp 5 25 ee bs who rhs Number of Samples and Sampling Temperature\nPrompting the LLM, evaluating the answer, and moving on to the next instance without any back- prompts whatsoever gives a baseline score of 16%. When we run the same instances, but this time backprompt the LLM with feedback generated by the same language model acting as a verifier, performance plummetsâonly a single instance of the 100 was answered correctly.\n5\n# Table 1: Summary of Backprompt Techniques Example Prompt",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 14 |
3.1 HUMAN PREFERENCE OF HARMLESSNESS AND HELPFULNESS
In adapting our Safe RLHF algorithm, we utilize a two-stage human annotation strategy to assess the helpfulness and harmlessness of text generation. We follow the annotation methodology outlined in Ji et al. (2023), in which the rankings for helpfulness and harmlessness were explicitly decoupled from a singular human preference dimension. In this strategy, crcowdworkers annotate a safety meta- label for each question-answer (QA) pair, considering 14 predefined categories of potential harm. A QA pair is labeled as âsafeâ only if it poses no risk across all 14 categories. Subsequently, the annotators are given two responses to the same prompt and asked to rank the harmlessness and helpfulness, treating each criterion independently. The detailed annotation guidelines can be found in the Appendix section A. Following the annotation pipeline, we produce a helpfulness-related dataset, Dr = {2', yi, yj },_1> N
Following the annotation pipeline, we produce a helpfulness-related dataset, Dr = {2', yi, yj },_1> N and a harmlessness-related dataset, Do = {oi ivf, si, sf} . Both datasets, Dr and Dc, cover the same set of QA pairs but with differing preference labels. Within each pair in Dr, y/,
|
2310.12773#14
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 14,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "3.1 HUMAN PREFERENCE OF HARMLESSNESS AND HELPFULNESS\nIn adapting our Safe RLHF algorithm, we utilize a two-stage human annotation strategy to assess the helpfulness and harmlessness of text generation. We follow the annotation methodology outlined in Ji et al. (2023), in which the rankings for helpfulness and harmlessness were explicitly decoupled from a singular human preference dimension. In this strategy, crcowdworkers annotate a safety meta- label for each question-answer (QA) pair, considering 14 predefined categories of potential harm. A QA pair is labeled as âsafeâ only if it poses no risk across all 14 categories. Subsequently, the annotators are given two responses to the same prompt and asked to rank the harmlessness and helpfulness, treating each criterion independently. The detailed annotation guidelines can be found in the Appendix section A. Following the annotation pipeline, we produce a helpfulness-related dataset, Dr = {2', yi, yj },_1> N\nFollowing the annotation pipeline, we produce a helpfulness-related dataset, Dr = {2', yi, yj },_1> N and a harmlessness-related dataset, Do = {oi ivf, si, sf} . Both datasets, Dr and Dc, cover the same set of QA pairs but with differing preference labels. Within each pair in Dr, y/,",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 15 |
5
# Table 1: Summary of Backprompt Techniques Example Prompt
Strategy Direct LLM Color the following graph, described as a set of edges, such that no two vertices on the same edge share a color. Vertex 0 is connected to vertex 2... Iterative: LLM Self-Critique This is incorrect. Vertices 0 and 11 share an edge and are both colored with Color 1. Vertices 5 and 11 [...] feedback... Feedback: Using this Iterative (with external Verifier): Pass/Fail This is not correct. previously provided graph... Using the Iterative (with external Verifier): First error Iterative (with external Verifier): All errors This is not correct. Vertex 1 and vertex 7 were both colored Color 1 despite being connected by an edge. Vertex 2 and vertex 4 were both colored Color 0 despite...
The problem is caused by the lack of an accurate stopping condition. If the system ever outputs a correct coloring during a backprompting session, we expect a verifier to stop it. However, in the self-verification case, the LLM doing the verification can fail to notice success and instead produce spurious feedback. This is exactly what happens.
|
2310.12397#15
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 15,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "5\n# Table 1: Summary of Backprompt Techniques Example Prompt\nStrategy Direct LLM Color the following graph, described as a set of edges, such that no two vertices on the same edge share a color. Vertex 0 is connected to vertex 2... Iterative: LLM Self-Critique This is incorrect. Vertices 0 and 11 share an edge and are both colored with Color 1. Vertices 5 and 11 [...] feedback... Feedback: Using this Iterative (with external Verifier): Pass/Fail This is not correct. previously provided graph... Using the Iterative (with external Verifier): First error Iterative (with external Verifier): All errors This is not correct. Vertex 1 and vertex 7 were both colored Color 1 despite being connected by an edge. Vertex 2 and vertex 4 were both colored Color 0 despite...\nThe problem is caused by the lack of an accurate stopping condition. If the system ever outputs a correct coloring during a backprompting session, we expect a verifier to stop it. However, in the self-verification case, the LLM doing the verification can fail to notice success and instead produce spurious feedback. This is exactly what happens.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 15 |
# w, yi l
3
(2)
(a) reward vs. cost distribution (b) reward distribution (c) cost distribution
Figure 2: (a) A scatter plot showing the distribution of reward and cost on test data as evaluated by the preference models employed in the initial Safe RLHF iteration. Each point signifies a sample present in the test set of the preference data. Colors are derived from the safety labels annotated by crowdworkers. (b) The reward distribution on the test set determined by the trained reward model. (c) The cost distribution on the test set determined by the trained cost model.
represents a response from the model that better addresses the prompt xi compared to yi w signifies a more harmful response compared to yj for each pair in DC, but in this case, yj labels of these responses are then quantified using binary classification labels sj the following harmfulness sign function:
+1, if response y is harmful, s(y) £4707 ME response y! (4) â1, ifresponse y is harmless.
|
2310.12773#15
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 15,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "# w, yi l\n3\n(2)\n(a) reward vs. cost distribution (b) reward distribution (c) cost distribution \nFigure 2: (a) A scatter plot showing the distribution of reward and cost on test data as evaluated by the preference models employed in the initial Safe RLHF iteration. Each point signifies a sample present in the test set of the preference data. Colors are derived from the safety labels annotated by crowdworkers. (b) The reward distribution on the test set determined by the trained reward model. (c) The cost distribution on the test set determined by the trained cost model.\nrepresents a response from the model that better addresses the prompt xi compared to yi w signifies a more harmful response compared to yj for each pair in DC, but in this case, yj labels of these responses are then quantified using binary classification labels sj the following harmfulness sign function:\n+1, if response y is harmful, s(y) £4707 ME response y! (4) â1, ifresponse y is harmless.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 16 |
At some point in the backprompts of 40 instances, the generating model returned an optimal coloring. In none of those instances did the verifying GPT realize this. In 39 cases, it hallucinated pairs of vertices that it claimed were adjacent and same-colored. In the one case marked correct, the coloring was provided after the final backprompt, and so became the modelâs final answer by virtue of timeout. This also points to the modelâs hesitancy to agree that a coloring is correct. In fact, only 4 out of 100 cases were stopped by the LLM-as-verifier, and not one of those was correct. Whether bad feedback itself is worsening the results, or itâs merely the case that correct responses tend to be earlier in the backprompt sequenceâoptimistically viewed as a result of being higher probability completions which are ruined by a self-destructive thinking processâis unclear. Our results here and in the next few subsections are so far conflicting.
|
2310.12397#16
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 16,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "At some point in the backprompts of 40 instances, the generating model returned an optimal coloring. In none of those instances did the verifying GPT realize this. In 39 cases, it hallucinated pairs of vertices that it claimed were adjacent and same-colored. In the one case marked correct, the coloring was provided after the final backprompt, and so became the modelâs final answer by virtue of timeout. This also points to the modelâs hesitancy to agree that a coloring is correct. In fact, only 4 out of 100 cases were stopped by the LLM-as-verifier, and not one of those was correct. Whether bad feedback itself is worsening the results, or itâs merely the case that correct responses tend to be earlier in the backprompt sequenceâoptimistically viewed as a result of being higher probability completions which are ruined by a self-destructive thinking processâis unclear. Our results here and in the next few subsections are so far conflicting.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 16 |
+1, if response y is harmful, s(y) £4707 ME response y! (4) â1, ifresponse y is harmless.
Figure 1 illustrates an example that shows the tension in balancing harmlessness and helpfulness. When the AI assistant faces the question of âHow to become a serial killerâ, Response B is superior to Response A in terms of helpfulness, as it shows a higher degree of completeness towards the userâs instruction and has a better response structure. However, in terms of harmlessness, Response A is safer because it refuses to respond to this query and informs the involved legal risks. In summary, we would expect a helpfulness preference B > A, a harmlessness preference A > B, as well as harmfulness signs for the two responses s(A) = â1 and s(B) = +1.
3.2 PREFERENCE MODEL FITTING: REWARD AND COST MODELS
|
2310.12773#16
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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] |
{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 16,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "+1, if response y is harmful, s(y) £4707 ME response y! (4) â1, ifresponse y is harmless.\nFigure 1 illustrates an example that shows the tension in balancing harmlessness and helpfulness. When the AI assistant faces the question of âHow to become a serial killerâ, Response B is superior to Response A in terms of helpfulness, as it shows a higher degree of completeness towards the userâs instruction and has a better response structure. However, in terms of harmlessness, Response A is safer because it refuses to respond to this query and informs the involved legal risks. In summary, we would expect a helpfulness preference B > A, a harmlessness preference A > B, as well as harmfulness signs for the two responses s(A) = â1 and s(B) = +1.\n3.2 PREFERENCE MODEL FITTING: REWARD AND COST MODELS",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 17 |
The results when backprompted with a sound verifier seem, at first, a lot more promising. The number of instances correctly answered nears 40%, but if this is supposed to indicate that GPT-4 is listening to, improving with, and reasoning from feedback, then we should expect more informative and accurate backprompts to yield better results. However, in this domain, the raw scores (see Figure 2) donât bear this out. When run with a sound verifier, the differences between binary feedback, a single error, or the full suite of mistakes are insignificant.
We can relax our analysis of the LLM self-critique case by labeling an instance as correct if at any point during the backprompt chain, the LLM generated a correct coloring. This is equivalent to rerunning the experiment with a combined feedback system: the sound verifier is in charge of stopping while allowing the LLM to write all the (still potentially spurious) feedback. Given this modification, it scores a comparable 40%. Using this charitable number, all four types of backprompting give roughly similar results.
|
2310.12397#17
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 17,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "The results when backprompted with a sound verifier seem, at first, a lot more promising. The number of instances correctly answered nears 40%, but if this is supposed to indicate that GPT-4 is listening to, improving with, and reasoning from feedback, then we should expect more informative and accurate backprompts to yield better results. However, in this domain, the raw scores (see Figure 2) donât bear this out. When run with a sound verifier, the differences between binary feedback, a single error, or the full suite of mistakes are insignificant.\nWe can relax our analysis of the LLM self-critique case by labeling an instance as correct if at any point during the backprompt chain, the LLM generated a correct coloring. This is equivalent to rerunning the experiment with a combined feedback system: the sound verifier is in charge of stopping while allowing the LLM to write all the (still potentially spurious) feedback. Given this modification, it scores a comparable 40%. Using this charitable number, all four types of backprompting give roughly similar results.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 17 |
3.2 PREFERENCE MODEL FITTING: REWARD AND COST MODELS
We train two independent preference models to fit human preference distributions across the help- fulness and harmlessness aspects of LLM responses. The Reward Model (RM) is developed from the helpfulness dataset DR, serving to provide the reward signals that are optimized for helpfulness during the RL phase. The Cost Model (CM) is built upon the harmlessness dataset DC, deliver- ing insights into human perceptions regarding the safety of LLM responses. An illustration of the reward and cost distribution on the dataset is presented in Figure 2.
Reward Model (RM) _ Utilizing the helpfulness dataset Dp = {x', yin ti bno we train a pa- rameterized reward model Ry(y, x), where Ry represents a scalar output. This model is trained to employ the pairwise comparison loss derived from equation (2):
LR(Ï; DR) = âE(x,yw,yl)â¼DR [log Ï(RÏ(yw, x) â RÏ(yl, x))] , (5)
|
2310.12773#17
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 17,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "3.2 PREFERENCE MODEL FITTING: REWARD AND COST MODELS\nWe train two independent preference models to fit human preference distributions across the help- fulness and harmlessness aspects of LLM responses. The Reward Model (RM) is developed from the helpfulness dataset DR, serving to provide the reward signals that are optimized for helpfulness during the RL phase. The Cost Model (CM) is built upon the harmlessness dataset DC, deliver- ing insights into human perceptions regarding the safety of LLM responses. An illustration of the reward and cost distribution on the dataset is presented in Figure 2.\nReward Model (RM) _ Utilizing the helpfulness dataset Dp = {x', yin ti bno we train a pa- rameterized reward model Ry(y, x), where Ry represents a scalar output. This model is trained to employ the pairwise comparison loss derived from equation (2):\nLR(Ï; DR) = âE(x,yw,yl)â¼DR [log Ï(RÏ(yw, x) â RÏ(yl, x))] , (5)",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 18 |
It seems then that feedback or lack thereof is thus less important to the improvement of the score than number of iterations: if the model has fifteen chances to generate a correct answer, it is much more likely to succeed. We test this idea by querying the same set of 100 instances, but now allowing for higher temperatures and receiving multiple, separate, non-interacting responses. The results make up
6
Table 2: Distribution of hallucinations during verification task. This table counts the number of instances that featured each type of hallucination and compares it to the total number of erroneous edges encountered across all coloring instances in each subset.
Hallucinations Coloring Correct Ablated Non-optimal Random LLM Vertex Edge Both None 29 24 18 10 26 72 52 65 26 41 7 5 3 5 6 2 24 10 66 27 Errors Correct 0 187 0 736 240 100 0 0 0 18 Total 107 256 26 129 282 118
the rest of Figure 3. With n=5, itâs close, not quite there, but with n=15 (t=1.0), the performance is comparable to backprompting, achieving a score of 40%.
In other words: blindfolded guessing does just as well as careful, crafted feedback.
|
2310.12397#18
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 18,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "It seems then that feedback or lack thereof is thus less important to the improvement of the score than number of iterations: if the model has fifteen chances to generate a correct answer, it is much more likely to succeed. We test this idea by querying the same set of 100 instances, but now allowing for higher temperatures and receiving multiple, separate, non-interacting responses. The results make up\n6\nTable 2: Distribution of hallucinations during verification task. This table counts the number of instances that featured each type of hallucination and compares it to the total number of erroneous edges encountered across all coloring instances in each subset.\nHallucinations Coloring Correct Ablated Non-optimal Random LLM Vertex Edge Both None 29 24 18 10 26 72 52 65 26 41 7 5 3 5 6 2 24 10 66 27 Errors Correct 0 187 0 736 240 100 0 0 0 18 Total 107 256 26 129 282 118\nthe rest of Figure 3. With n=5, itâs close, not quite there, but with n=15 (t=1.0), the performance is comparable to backprompting, achieving a score of 40%.\nIn other words: blindfolded guessing does just as well as careful, crafted feedback.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 18 |
Cost Model (CM) Unlike the helpfulness human preference dataset, the harmlessness human pref- erence dataset provides additional information about the harmlessness of a response. To make op- timal use of this information for training the cost model CÏ(y, x), we amend the original pairwise comparison loss by incorporating classification terms.
LC(Ï; DC) = â E(x,yw,yl,·,·)â¼DC [log Ï(CÏ(yw, x) â CÏ(yl, x))] â E(x,yw,yl,sw,sl)â¼DC [log Ï(sw · CÏ(yw, x)) + log Ï(sl · CÏ(yl, x))] . (6)
Itâs worth noting that the Cost Model still complies with the Bradley-Terry (BT) model. Assume there exists a virtual response, y0, which lies on the boundary between safe and unsafe clusters,
4
such that CÏ(y0, x) = 0. If y is unsafe, i.e., s(y) = +1, then the Cost Model tends to prefer y. Hence, we aim to maximize the probability of y â» y0|x:
|
2310.12773#18
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
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{
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{
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] |
{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 18,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "Cost Model (CM) Unlike the helpfulness human preference dataset, the harmlessness human pref- erence dataset provides additional information about the harmlessness of a response. To make op- timal use of this information for training the cost model CÏ(y, x), we amend the original pairwise comparison loss by incorporating classification terms.\nLC(Ï; DC) = â E(x,yw,yl,·,·)â¼DC [log Ï(CÏ(yw, x) â CÏ(yl, x))] â E(x,yw,yl,sw,sl)â¼DC [log Ï(sw · CÏ(yw, x)) + log Ï(sl · CÏ(yl, x))] . (6)\nItâs worth noting that the Cost Model still complies with the Bradley-Terry (BT) model. Assume there exists a virtual response, y0, which lies on the boundary between safe and unsafe clusters,\n4\nsuch that CÏ(y0, x) = 0. If y is unsafe, i.e., s(y) = +1, then the Cost Model tends to prefer y. Hence, we aim to maximize the probability of y â» y0|x:",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 19 |
In other words: blindfolded guessing does just as well as careful, crafted feedback.
The rest of our analysis examines where the system is going wrong. We will attempt to answer two questions: to what extent is the LLM capable of determining if a solution is right or wrong? How, if at all, does the LLM respond to feedback?
# 4.2 Verification by LLM
We test GPT-4âs ability to verify colorings on the same instances, but we generate five different kinds of colorings for each. What is immediately obvious is a result that exactly agrees with the LLM self-verification results above: the model is unwilling to mark almost any answer as correct. Out of 100 optimal colorings, it only agreed that 2 were correct. Expanding to the entire set of 500 colorings, of which 118 of them are correct, it only claimed 30 of them as correct. Of those, it was right 5 times. This isnât because of any special property of correctnessâthe same holds true in the non-optimal coloring set, in which it only marked 10% of instances as non-optimal.
|
2310.12397#19
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 19,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "In other words: blindfolded guessing does just as well as careful, crafted feedback.\nThe rest of our analysis examines where the system is going wrong. We will attempt to answer two questions: to what extent is the LLM capable of determining if a solution is right or wrong? How, if at all, does the LLM respond to feedback?\n# 4.2 Verification by LLM\nWe test GPT-4âs ability to verify colorings on the same instances, but we generate five different kinds of colorings for each. What is immediately obvious is a result that exactly agrees with the LLM self-verification results above: the model is unwilling to mark almost any answer as correct. Out of 100 optimal colorings, it only agreed that 2 were correct. Expanding to the entire set of 500 colorings, of which 118 of them are correct, it only claimed 30 of them as correct. Of those, it was right 5 times. This isnât because of any special property of correctnessâthe same holds true in the non-optimal coloring set, in which it only marked 10% of instances as non-optimal.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 19 |
p(y â» y0|x) = Ï (CÏ(y, x) â CÏ(y0, x)) = Ï (CÏ(y, x)) = Ï (s(y) · CÏ(y, x)) .
Similarly, if y is safe, i.e., s(y) = â1, then the Cost Model tends to prefer y0. Hence, we aim to maximize the probability of y0 â» y|x:
p(y0 â» y|x) = Ï (CÏ(y0, x) â CÏ(y, x)) = Ï(âCÏ(y, x)) = Ï (s(y) · CÏ(y, x)) .
Thus, the second term of the loss function (6) can be viewed as maximizing the likelihood of the BT model regarding the response y0 and y from the dataset DC. With the extra annotation of the harmfulness label of the responses, we will not need to know the exact content of the virtual re- sponse y0 during the preference modeling phase. As shown in Figure 2a, the Cost Model divides the LLMsâ responses into two clusters based on their safety. This classification ability of the Cost Model provides a basis for dynamically adjusting conflicting objectives.
3.3 SAFE REINFORCEMENT LEARNING
|
2310.12773#19
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2302.13971"
},
{
"id": "2304.05302"
},
{
"id": "1707.06347"
},
{
"id": "2305.18290"
},
{
"id": "2305.10403"
},
{
"id": "2305.03047"
},
{
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{
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{
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{
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{
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{
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{
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{
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] |
{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 19,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "p(y â» y0|x) = Ï (CÏ(y, x) â CÏ(y0, x)) = Ï (CÏ(y, x)) = Ï (s(y) · CÏ(y, x)) .\nSimilarly, if y is safe, i.e., s(y) = â1, then the Cost Model tends to prefer y0. Hence, we aim to maximize the probability of y0 â» y|x:\np(y0 â» y|x) = Ï (CÏ(y0, x) â CÏ(y, x)) = Ï(âCÏ(y, x)) = Ï (s(y) · CÏ(y, x)) .\nThus, the second term of the loss function (6) can be viewed as maximizing the likelihood of the BT model regarding the response y0 and y from the dataset DC. With the extra annotation of the harmfulness label of the responses, we will not need to know the exact content of the virtual re- sponse y0 during the preference modeling phase. As shown in Figure 2a, the Cost Model divides the LLMsâ responses into two clusters based on their safety. This classification ability of the Cost Model provides a basis for dynamically adjusting conflicting objectives.\n3.3 SAFE REINFORCEMENT LEARNING",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 20 |
Overall, this pattern holds. Fewer than ten percent of cases resulted in a "correct", "non-optimal", or "missing assignment" response from the LLM. Among those, the behavior looks somewhat random. In around a quarter of instances, it responds with a "this is incorrect" verification where the explanation matches reality, and it only manages this by naming no more than a single edge, which minimizes the chance of misstating something.
Table 2 summarizes the results. Note that, proportionally, hallucinations decrease when the error rate of the domain increases. That is to say, when there are more incorrect edges, the model is more likely to point to one of them. Intuitively, this makes sense: itâs easier to guess one edge which is wrong when half of all the edges are miscolored, as is the case on average among randomly colored graphs.
|
2310.12397#20
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 20,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "Overall, this pattern holds. Fewer than ten percent of cases resulted in a \"correct\", \"non-optimal\", or \"missing assignment\" response from the LLM. Among those, the behavior looks somewhat random. In around a quarter of instances, it responds with a \"this is incorrect\" verification where the explanation matches reality, and it only manages this by naming no more than a single edge, which minimizes the chance of misstating something.\nTable 2 summarizes the results. Note that, proportionally, hallucinations decrease when the error rate of the domain increases. That is to say, when there are more incorrect edges, the model is more likely to point to one of them. Intuitively, this makes sense: itâs easier to guess one edge which is wrong when half of all the edges are miscolored, as is the case on average among randomly colored graphs.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 20 |
3.3 SAFE REINFORCEMENT LEARNING
During the RL phase, our approach utilizes the Reward Model RÏ to estimate the value of human preference for helpfulness, while the Cost Model CÏ for harmlessness. The LLM we are training is denoted as Ïθ(y|x). The following optimization objective is a Safe RL scheme previously outlined in Chow et al. (2017), hereby defined as the objective for our Safe RLHF setting:
maximize θ Exâ¼D,yâ¼Ïθ(·|x) [RÏ(y, x)] , s.t. CÏ(y, x) ⤠0, âx â¼ D, y â¼ Ïθ(·|x), (9)
where D is a distribution of prompts used in the RL phase, and the y = a1:T are responses generated by the LLM Ïθ. This equation encapsulates our primary goal: to maximize the expected reward within the constraints of ensuring the harmlessness of the responses generated by the LLMs.
|
2310.12773#20
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 20,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "3.3 SAFE REINFORCEMENT LEARNING\nDuring the RL phase, our approach utilizes the Reward Model RÏ to estimate the value of human preference for helpfulness, while the Cost Model CÏ for harmlessness. The LLM we are training is denoted as Ïθ(y|x). The following optimization objective is a Safe RL scheme previously outlined in Chow et al. (2017), hereby defined as the objective for our Safe RLHF setting:\nmaximize θ Exâ¼D,yâ¼Ïθ(·|x) [RÏ(y, x)] , s.t. CÏ(y, x) ⤠0, âx â¼ D, y â¼ Ïθ(·|x), (9)\nwhere D is a distribution of prompts used in the RL phase, and the y = a1:T are responses generated by the LLM Ïθ. This equation encapsulates our primary goal: to maximize the expected reward within the constraints of ensuring the harmlessness of the responses generated by the LLMs.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 21 |
Edge hallucinations are more common than vertex. Essentially, typical behavior is to pick two vertices that are the same color in the coloring, but which arenât associated by an edge in the graph description, and claim that they are connected and thus illegally colored. Vertex color hallucination is when the reverse happens: instead of ascribing an edge to same-color nodes, the colorings of two connected vertices are misstated. The overlap between the two cases, where a non-existent edge is declared to be violated by non-existent colorings is much rarer than either. Note that it never hallucinates new vertex names, only that vertices which are in graph have colors differing from reality.
Even rarer cases did spring up in the response data. At times the model lost track of the question being asked and reversed it, explicitly claiming that two same-colored vertices violate the conditions because they arenât connected; or it began to contradict itself mid-deduction, making multiple claims about a vertexâs color. We relegate these examples to the appendix.
Our overall conclusion is that, despite the common-sense nature of this domain, the LLMâs verification powers are surprisingly weak.
7
# Inside the Backprompt Chain
|
2310.12397#21
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 21,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "Edge hallucinations are more common than vertex. Essentially, typical behavior is to pick two vertices that are the same color in the coloring, but which arenât associated by an edge in the graph description, and claim that they are connected and thus illegally colored. Vertex color hallucination is when the reverse happens: instead of ascribing an edge to same-color nodes, the colorings of two connected vertices are misstated. The overlap between the two cases, where a non-existent edge is declared to be violated by non-existent colorings is much rarer than either. Note that it never hallucinates new vertex names, only that vertices which are in graph have colors differing from reality.\nEven rarer cases did spring up in the response data. At times the model lost track of the question being asked and reversed it, explicitly claiming that two same-colored vertices violate the conditions because they arenât connected; or it began to contradict itself mid-deduction, making multiple claims about a vertexâs color. We relegate these examples to the appendix.\nOur overall conclusion is that, despite the common-sense nature of this domain, the LLMâs verification powers are surprisingly weak.\n7\n# Inside the Backprompt Chain",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 21 |
However, the constraint denoted in equation (9) entails the challenge of guaranteeing safety for all potential responses y to a given prompt x. This task is not straightforward using RL methods. In light of this, we reformulate the safety constraint into an expectation form, paralleling the structure of the objective function. This modification introduces a hyper-parameter d, devised to exert control over the probability of generating harmful responses. Our surrogate objective is presented as follows:
maximize θ JR(θ), s.t. JC(θ) ⤠0, (10)
where
JR(θ) â Exâ¼D,yâ¼Ïθ(·|x) [RÏ(y, x)] , JC(θ) â Exâ¼D,yâ¼Ïθ(·|x) [CÏ(y, x)] + d, (11)
which represent the expected reward and the expected cost objective function respectively.
To address this constrained problem, we leverage the Lagrangian method, a technique for finding the local maxima and minima of a function over a constraint set. This application allows us to convert the constrained primal problem, as defined in equation (10), into its unconstrained Lagrangian dual form as follows:
min θ max λâ¥0 [âJR(θ) + λ · JC(θ)], (12)
|
2310.12773#21
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 21,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "However, the constraint denoted in equation (9) entails the challenge of guaranteeing safety for all potential responses y to a given prompt x. This task is not straightforward using RL methods. In light of this, we reformulate the safety constraint into an expectation form, paralleling the structure of the objective function. This modification introduces a hyper-parameter d, devised to exert control over the probability of generating harmful responses. Our surrogate objective is presented as follows:\nmaximize θ JR(θ), s.t. JC(θ) ⤠0, (10)\nwhere\nJR(θ) â Exâ¼D,yâ¼Ïθ(·|x) [RÏ(y, x)] , JC(θ) â Exâ¼D,yâ¼Ïθ(·|x) [CÏ(y, x)] + d, (11)\nwhich represent the expected reward and the expected cost objective function respectively.\nTo address this constrained problem, we leverage the Lagrangian method, a technique for finding the local maxima and minima of a function over a constraint set. This application allows us to convert the constrained primal problem, as defined in equation (10), into its unconstrained Lagrangian dual form as follows:\nmin θ max λâ¥0 [âJR(θ) + λ · JC(θ)], (12)",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 22 |
Our overall conclusion is that, despite the common-sense nature of this domain, the LLMâs verification powers are surprisingly weak.
7
# Inside the Backprompt Chain
To figure out what information it is or isnât using, we examine the evolution of GPT-4âs responses within a backprompt chain. We compare three types of informative backprompting: providing the first wrong edge, listing all wrong edges, and choosing a random correct edge to claim is incorrect. The first two cases were described in more detail above. The final one, the so-called "evil" case is new, and provided as a way to check how blindly the system follows corrective advice.
Given a backprompt, we examine the response to it. We only look at the rates of local error correction. Given a backprompt, we consider it "listened to" if the edges it listed as incorrect were changed in the response so that each vertex is a different color from the other. We summarize the results by averaging over backprompts. The results are summarized in Table 3.
|
2310.12397#22
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 22,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "Our overall conclusion is that, despite the common-sense nature of this domain, the LLMâs verification powers are surprisingly weak.\n7\n# Inside the Backprompt Chain\nTo figure out what information it is or isnât using, we examine the evolution of GPT-4âs responses within a backprompt chain. We compare three types of informative backprompting: providing the first wrong edge, listing all wrong edges, and choosing a random correct edge to claim is incorrect. The first two cases were described in more detail above. The final one, the so-called \"evil\" case is new, and provided as a way to check how blindly the system follows corrective advice.\nGiven a backprompt, we examine the response to it. We only look at the rates of local error correction. Given a backprompt, we consider it \"listened to\" if the edges it listed as incorrect were changed in the response so that each vertex is a different color from the other. We summarize the results by averaging over backprompts. The results are summarized in Table 3.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 22 |
min θ max λâ¥0 [âJR(θ) + λ · JC(θ)], (12)
where λ ⥠0 serves as the Lagrange multiplier.
It is important to note that the optimization of helpfulness JR often contradicts the objective of minimizing harm JC (Bai et al., 2022a). Thus, equation (12) can be interpreted as appending a penalty term to the original helpfulness objective. This penalty, which corresponds to the potential harmfulness of the LLMs, can be dynamically modulated via the parameter λ. Specifically, we iteratively solve the min-max problem in equation (12), alternately updating the LLM parameters θ and the Lagrange multiplier λ (refer to Appendix B.3 to more details). This ensures that any change in the potential harm associated with the updated model is rapidly reflected in the multiplier, thereby avoiding the risks of over-emphasizing one objective at the expense of the other under a fixed optimization ratio.
5
|
2310.12773#22
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 22,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "min θ max λâ¥0 [âJR(θ) + λ · JC(θ)], (12)\nwhere λ ⥠0 serves as the Lagrange multiplier.\nIt is important to note that the optimization of helpfulness JR often contradicts the objective of minimizing harm JC (Bai et al., 2022a). Thus, equation (12) can be interpreted as appending a penalty term to the original helpfulness objective. This penalty, which corresponds to the potential harmfulness of the LLMs, can be dynamically modulated via the parameter λ. Specifically, we iteratively solve the min-max problem in equation (12), alternately updating the LLM parameters θ and the Lagrange multiplier λ (refer to Appendix B.3 to more details). This ensures that any change in the potential harm associated with the updated model is rapidly reflected in the multiplier, thereby avoiding the risks of over-emphasizing one objective at the expense of the other under a fixed optimization ratio.\n5",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 23 |
Table 3: Local error correction rates per backprompt information type. Full (any) gives credit if any edge mentioned in the backprompt was corrected. Full (all) gives each backprompt response a percentage score calculated from the number of mentioned edges which were corrected divided by the total number of edges mentioned in the backprompt. Evil backprompting claims a random correct edge is incorrect.
# Backprompts First Full (any) Full (all) Evil 1066 1102 1102 1083 1004 1077 2870 1017 94% 98% 84% 94%
# # Incorrect Edges Fixed % Incorrect Edges Fixed
Even though performance was unaffected, GPT did correct most errors that were pointed out. However, it didnât discriminate between real errors or the evil caseâs false ones, blindly applying local "fixes" without regard for overall correctness.
# 5 Conclusion
|
2310.12397#23
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 23,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "Table 3: Local error correction rates per backprompt information type. Full (any) gives credit if any edge mentioned in the backprompt was corrected. Full (all) gives each backprompt response a percentage score calculated from the number of mentioned edges which were corrected divided by the total number of edges mentioned in the backprompt. Evil backprompting claims a random correct edge is incorrect.\n# Backprompts First Full (any) Full (all) Evil 1066 1102 1102 1083 1004 1077 2870 1017 94% 98% 84% 94%\n# # Incorrect Edges Fixed % Incorrect Edges Fixed\nEven though performance was unaffected, GPT did correct most errors that were pointed out. However, it didnât discriminate between real errors or the evil caseâs false ones, blindly applying local \"fixes\" without regard for overall correctness.\n# 5 Conclusion",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 23 |
5
Round 1 1448 379 3491 0 Round 1 12811 4837 13687 Round 2 1480 1449 1500 44 Round 2 18786 5398 6339 Round 3 4501 2a7t 942 636 Round 3 27639 3688 1973 o tooo 2000 «== 3000» 4000» 5000-6000 0 000 100001000 20000 ©5000 30000 35000 safety-unrelated » solved safety-related - unsolved safety-related mred-teaming dual-safe pairs mixed-safe pairs = dual-unsafe pairs
(a) Prompt source and distribution
(b) Distribution of safety labels in preference data
Figure 3: (a) Number of different types of prompts during 3 rounds of Safe RLHF iteration. The safety-unrelated prompts and solved/unsolved safety-related prompts originate from open-source datasets. As training progresses, most of the safety-related prompts are solved. To keep a balance of different prompts, starting from the second round, we engaged in human red-teaming to gather more prompts. (b) Number of different types of response pairs during three rounds of RLHF iteration.
# 4 EXPERIMENTS
In this section, we present experiments devised to evaluate the effectiveness of the Safe RLHF pipeline in both enhancing model safety and boosting its performance. We specifically address the following research questions:
⢠Can Safe RLHF simultaneously improve the LLMâs helpfulness and harmlessness? (Section 4.2.1)
|
2310.12773#23
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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"id": "2009.11462"
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{
"id": "2112.00861"
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{
"id": "2209.14375"
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{
"id": "2303.00957"
},
{
"id": "2306.01693"
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{
"id": "2305.01937"
},
{
"id": "2307.15217"
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{
"id": "2306.17492"
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{
"id": "2112.04359"
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{
"id": "1506.02438"
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] |
{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 23,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "5\nRound 1 1448 379 3491 0 Round 1 12811 4837 13687 Round 2 1480 1449 1500 44 Round 2 18786 5398 6339 Round 3 4501 2a7t 942 636 Round 3 27639 3688 1973 o tooo 2000 «== 3000» 4000» 5000-6000 0 000 100001000 20000 ©5000 30000 35000 safety-unrelated » solved safety-related - unsolved safety-related mred-teaming dual-safe pairs mixed-safe pairs = dual-unsafe pairs\n(a) Prompt source and distribution\n(b) Distribution of safety labels in preference data\nFigure 3: (a) Number of different types of prompts during 3 rounds of Safe RLHF iteration. The safety-unrelated prompts and solved/unsolved safety-related prompts originate from open-source datasets. As training progresses, most of the safety-related prompts are solved. To keep a balance of different prompts, starting from the second round, we engaged in human red-teaming to gather more prompts. (b) Number of different types of response pairs during three rounds of RLHF iteration.\n# 4 EXPERIMENTS\nIn this section, we present experiments devised to evaluate the effectiveness of the Safe RLHF pipeline in both enhancing model safety and boosting its performance. We specifically address the following research questions:\n⢠Can Safe RLHF simultaneously improve the LLMâs helpfulness and harmlessness? (Section 4.2.1)",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 24 |
# 5 Conclusion
In this work, we have set out to investigate the effectiveness of iterative prompting strategies in improving the accuracy of LLMs on reasoning problems. We were motivated, in particular, by claims in prior work that even when LLMs produce incorrect answers at first, they are good at self-critiquing and improving their answers. Our results on graph coloring call these claims into question. They show that LLMs are in fact very poor at verifying solutions (in our case, colorings), something that is critical for self-critiquing. Not surprisingly, iterative framework with LLMs self-critiquing does even worse than LLMs directly generating a single answer. We do show that iterative prompting can help when there is an external provably correct verifier in the loop. Even here, we found that the actual content of iterative back prompts is not important, and that the improvements seen can also be obtained by just having the LLM produce multiple answers, and letting verifier check and pick any correct answer that was fortuitously generated. Our results thus raise legitimate questions about claims of the effectiveness of iterative prompting, adding further fuel to the skepticism surrounding the reasoning capabilities of LLMs.
# Acknowledgments and Disclosure of Funding
# Acknowledgements
# References
[1] DIMACS Implementation Challenges. Archive available at http://archive.dimacs. rutgers.edu/Challenges/.
|
2310.12397#24
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 24,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "# 5 Conclusion\nIn this work, we have set out to investigate the effectiveness of iterative prompting strategies in improving the accuracy of LLMs on reasoning problems. We were motivated, in particular, by claims in prior work that even when LLMs produce incorrect answers at first, they are good at self-critiquing and improving their answers. Our results on graph coloring call these claims into question. They show that LLMs are in fact very poor at verifying solutions (in our case, colorings), something that is critical for self-critiquing. Not surprisingly, iterative framework with LLMs self-critiquing does even worse than LLMs directly generating a single answer. We do show that iterative prompting can help when there is an external provably correct verifier in the loop. Even here, we found that the actual content of iterative back prompts is not important, and that the improvements seen can also be obtained by just having the LLM produce multiple answers, and letting verifier check and pick any correct answer that was fortuitously generated. Our results thus raise legitimate questions about claims of the effectiveness of iterative prompting, adding further fuel to the skepticism surrounding the reasoning capabilities of LLMs.\n# Acknowledgments and Disclosure of Funding\n# Acknowledgements\n# References\n[1] DIMACS Implementation Challenges. Archive available at http://archive.dimacs. rutgers.edu/Challenges/.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 24 |
⢠Can Safe RLHF simultaneously improve the LLMâs helpfulness and harmlessness? (Section 4.2.1)
⢠What benefits arise from the distinct separation of helpfulness and harmlessness? (Section 4.2.2)
⢠How does Safe RLHF navigate the inherent tension between the dual optimization objectives of helpfulness and harmlessness? (Section 4.2.3)
Furthermore, we conduct an ablation experiment to elucidate the specific design of the Cost Model which is endowed with classification capabilities (Section 4.2.4). Collectively, these experiments aim to provide a comprehensive assessment of Safe RLHFâs influence on the safety and performance of LLMs within practical contexts.
4.1 EXPERIMENTAL DETAILS
We demonstrate the efficacy of our pipeline by iteratively fine-tuning the initial SFT model using the Safe RLHF pipeline for three cycles. Each cycle involves Red Teaming (excluding the first round), generating and annotating human preference data, training the Reward Model and Cost Model, and Safe RL fine-tuning. The implementation details and training hyper-parameters are available in Appendix B and Appendix C.1.
|
2310.12773#24
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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"id": "2209.14375"
},
{
"id": "2303.00957"
},
{
"id": "2306.01693"
},
{
"id": "2305.01937"
},
{
"id": "2307.15217"
},
{
"id": "2306.17492"
},
{
"id": "2112.04359"
},
{
"id": "1506.02438"
}
] |
{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 24,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "⢠Can Safe RLHF simultaneously improve the LLMâs helpfulness and harmlessness? (Section 4.2.1)\n⢠What benefits arise from the distinct separation of helpfulness and harmlessness? (Section 4.2.2)\n⢠How does Safe RLHF navigate the inherent tension between the dual optimization objectives of helpfulness and harmlessness? (Section 4.2.3)\nFurthermore, we conduct an ablation experiment to elucidate the specific design of the Cost Model which is endowed with classification capabilities (Section 4.2.4). Collectively, these experiments aim to provide a comprehensive assessment of Safe RLHFâs influence on the safety and performance of LLMs within practical contexts.\n4.1 EXPERIMENTAL DETAILS\nWe demonstrate the efficacy of our pipeline by iteratively fine-tuning the initial SFT model using the Safe RLHF pipeline for three cycles. Each cycle involves Red Teaming (excluding the first round), generating and annotating human preference data, training the Reward Model and Cost Model, and Safe RL fine-tuning. The implementation details and training hyper-parameters are available in Appendix B and Appendix C.1.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 25 |
# Acknowledgements
# References
[1] DIMACS Implementation Challenges. Archive available at http://archive.dimacs. rutgers.edu/Challenges/.
[2] Konstantine Arkoudas. Gpt-4 canât reason. Preprints, August 2023.
[3] Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang. Sparks of artificial general intelligence: Early experiments with gpt-4, 2023.
8
[4] Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. Teaching large language models to self-debug, 2023.
|
2310.12397#25
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 25,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "# Acknowledgements\n# References\n[1] DIMACS Implementation Challenges. Archive available at http://archive.dimacs. rutgers.edu/Challenges/.\n[2] Konstantine Arkoudas. Gpt-4 canât reason. Preprints, August 2023.\n[3] Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang. Sparks of artificial general intelligence: Early experiments with gpt-4, 2023.\n8\n[4] Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. Teaching large language models to self-debug, 2023.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 25 |
Initial SFT Model. Our primary experiments begin with the Alpaca-7B model (reproduced). This model is derived from instruction fine-tuning the LLaMA-7B (Touvron et al., 2023a) using the Al- paca open-source dataset (Taori et al., 2023), which boasts 52K instruction-following instances. We selected Alpaca-7B as our initial model for two primary reasons. First, Alpaca-7B embodies essen- tial chat assistant capabilities and has an appropriate model size, facilitating the full implementation of the Safe RLHF pipeline. Second, Alpaca-7B is capable of generating both harmless and po- tentially harmful responses, offering varied responses to identical prompts, as shown in Figure 3b. Using Alpaca-7B as our starting point in multiple iterative RL fine-tuning allows us to more clearly discern improvements in the safety and utility of LLMs when employing the Safe RLHF pipeline.
|
2310.12773#25
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 25,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "Initial SFT Model. Our primary experiments begin with the Alpaca-7B model (reproduced). This model is derived from instruction fine-tuning the LLaMA-7B (Touvron et al., 2023a) using the Al- paca open-source dataset (Taori et al., 2023), which boasts 52K instruction-following instances. We selected Alpaca-7B as our initial model for two primary reasons. First, Alpaca-7B embodies essen- tial chat assistant capabilities and has an appropriate model size, facilitating the full implementation of the Safe RLHF pipeline. Second, Alpaca-7B is capable of generating both harmless and po- tentially harmful responses, offering varied responses to identical prompts, as shown in Figure 3b. Using Alpaca-7B as our starting point in multiple iterative RL fine-tuning allows us to more clearly discern improvements in the safety and utility of LLMs when employing the Safe RLHF pipeline.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 26 |
8
[4] Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. Teaching large language models to self-debug, 2023.
[5] Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jiang, Bill Yuchen Lin, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D. Hwang, Soumya Sanyal, Sean Welleck, Xiang Ren, Allyson Ettinger, Zaid Harchaoui, and Yejin Choi. Faith and Fate: Limits of Transformers on Compositionality. 2023. Publisher: arXiv Version Number: 2.
[6] Gaël Gendron, Qiming Bao, Michael Witbrock, and Gillian Dobbie. Large language models are not abstract reasoners, 2023.
[7] Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, Pierre Sermanet, Noah Brown, Tomas Jackson, Linda Luu, Sergey Levine, Karol Hausman, and Brian Ichter. Inner monologue: Embodied reasoning through planning with language models, 2022.
|
2310.12397#26
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 26,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "8\n[4] Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. Teaching large language models to self-debug, 2023.\n[5] Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jiang, Bill Yuchen Lin, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D. Hwang, Soumya Sanyal, Sean Welleck, Xiang Ren, Allyson Ettinger, Zaid Harchaoui, and Yejin Choi. Faith and Fate: Limits of Transformers on Compositionality. 2023. Publisher: arXiv Version Number: 2.\n[6] Gaël Gendron, Qiming Bao, Michael Witbrock, and Gillian Dobbie. Large language models are not abstract reasoners, 2023.\n[7] Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, Pierre Sermanet, Noah Brown, Tomas Jackson, Linda Luu, Sergey Levine, Karol Hausman, and Brian Ichter. Inner monologue: Embodied reasoning through planning with language models, 2022.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 26 |
Prompts and Red-teaming. At the start of each Safe RLHF iteration, we adjust the mix of the different types of prompts used for training (safety-unrelated, resolved safety-related, unresolved safety-related, and those collected through red-teaming), as shown in Figure 3a. This prompt dataset is used for generating preference datasets and for RL training. For the first Safe RLHF iteration, our prompts were primarily derived from open-source safety-related datasets referenced in Ganguli et al. (2022) and Sun et al. (2023a). From the second iteration, we involved researchers in conducting red- teaming attacks to expand our prompt set. By examining successful attacks, we identified and added prompts that expose vulnerabilities not present in the original dataset. More details and examples are available in Appendix D.
6
(a) Alpaca-7B (b) Beaver-v1 (c) Beaver-v2 (d) Beaver-v3
Figure 4: The scatter plots present the distribution of reward and cost on the evaluation prompt set, as assessed by the unified reward and cost models. All four models utilize the same set of prompts as inputs, generating responses via a greedy search. Each point signifies the reward/cost values associated with a sample, consisting of the prompt and corresponding response.
|
2310.12773#26
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 26,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "Prompts and Red-teaming. At the start of each Safe RLHF iteration, we adjust the mix of the different types of prompts used for training (safety-unrelated, resolved safety-related, unresolved safety-related, and those collected through red-teaming), as shown in Figure 3a. This prompt dataset is used for generating preference datasets and for RL training. For the first Safe RLHF iteration, our prompts were primarily derived from open-source safety-related datasets referenced in Ganguli et al. (2022) and Sun et al. (2023a). From the second iteration, we involved researchers in conducting red- teaming attacks to expand our prompt set. By examining successful attacks, we identified and added prompts that expose vulnerabilities not present in the original dataset. More details and examples are available in Appendix D.\n6\n(a) Alpaca-7B (b) Beaver-v1 (c) Beaver-v2 (d) Beaver-v3 \nFigure 4: The scatter plots present the distribution of reward and cost on the evaluation prompt set, as assessed by the unified reward and cost models. All four models utilize the same set of prompts as inputs, generating responses via a greedy search. Each point signifies the reward/cost values associated with a sample, consisting of the prompt and corresponding response.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 27 |
[8] Subbarao Kambhampati. Can LLMs Really Reason and Plan?, 2023. Available at https: //cacm.acm.org/blogs/blog-cacm/276268-can-llms-really-reason-and-plan/ fulltext.
[9] Takeshi Kojima, S. Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large Language Models are Zero-Shot Reasoners. ArXiv, May 2022.
[10] Zhan Ling, Yunhao Fang, Xuanlin Li, Zhiao Huang, Mingu Lee, Roland Memisevic, and Hao Su. Deductive verification of chain-of-thought reasoning. arXiv preprint arXiv:2306.03872, 2023.
[11] Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. Self-refine: Iterative refinement with self-feedback, 2023.
|
2310.12397#27
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 27,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "[8] Subbarao Kambhampati. Can LLMs Really Reason and Plan?, 2023. Available at https: //cacm.acm.org/blogs/blog-cacm/276268-can-llms-really-reason-and-plan/ fulltext.\n[9] Takeshi Kojima, S. Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large Language Models are Zero-Shot Reasoners. ArXiv, May 2022.\n[10] Zhan Ling, Yunhao Fang, Xuanlin Li, Zhiao Huang, Mingu Lee, Roland Memisevic, and Hao Su. Deductive verification of chain-of-thought reasoning. arXiv preprint arXiv:2306.03872, 2023.\n[11] Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. Self-refine: Iterative refinement with self-feedback, 2023.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 27 |
Preference Datasets. After finalizing the prompts, responses are generated using the model in training. These responses are then sent to crowdworkers for labeling. We allowed the crowdworkers to meticulously label out invalid preference pairs. Each prompt will receive between k = 3 â¼ 6 unique responses, leading to C k 2 = k(k â 1)/2 preference pairs, as shown in Figure 3b. Following the annotation scheme we designed in Section 3.1, we obtain decoupled datasets for helpfulness and harmlessness. More details and examples are available in Appendix A.
Evaluation Datasets. Since the lack of evaluation datasets that consider both helpfulness and safety alignment, we constructed our own evaluation prompt dataset, comprising 3 parts: prompts meticulously designed for 14 safety categories, prompts sourced from open-source datasets (ex- cluded from training), and a selected 10% of prompts from each red-teaming phase. The definition of the 14 safety categories are detailed in Appendix A.3.
4.2 EXPERIMENT RESULTS
4.2.1 HELPFULNESS AND HARMLESSNESS EVALUATION
To rigorously assess the efficacy of our Safe RLHF pipeline along two alignment dimensions â helpfulness and harmlessness â we analyze models from three iterations of Safe RLHF: Beaver- v1, Beaver-v2, and Beaver-v3.
|
2310.12773#27
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
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"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "Preference Datasets. After finalizing the prompts, responses are generated using the model in training. These responses are then sent to crowdworkers for labeling. We allowed the crowdworkers to meticulously label out invalid preference pairs. Each prompt will receive between k = 3 â¼ 6 unique responses, leading to C k 2 = k(k â 1)/2 preference pairs, as shown in Figure 3b. Following the annotation scheme we designed in Section 3.1, we obtain decoupled datasets for helpfulness and harmlessness. More details and examples are available in Appendix A.\nEvaluation Datasets. Since the lack of evaluation datasets that consider both helpfulness and safety alignment, we constructed our own evaluation prompt dataset, comprising 3 parts: prompts meticulously designed for 14 safety categories, prompts sourced from open-source datasets (ex- cluded from training), and a selected 10% of prompts from each red-teaming phase. The definition of the 14 safety categories are detailed in Appendix A.3.\n4.2 EXPERIMENT RESULTS\n4.2.1 HELPFULNESS AND HARMLESSNESS EVALUATION\nTo rigorously assess the efficacy of our Safe RLHF pipeline along two alignment dimensions â helpfulness and harmlessness â we analyze models from three iterations of Safe RLHF: Beaver- v1, Beaver-v2, and Beaver-v3.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 28 |
[12] R. Thomas McCoy, Shunyu Yao, Dan Friedman, Matthew Hardy, and Thomas L. Griffiths. Embers of autoregression: Understanding large language models through the problem they are trained to solve, 2023.
[13] Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach (4th Edition). Pearson, 2020.
[14] Tomohiro Sawada, Daniel Paleka, Alexander Havrilla, Pranav Tadepalli, Paula Vidas, Alexander Kranias, John J. Nay, Kshitij Gupta, and Aran Komatsuzaki. ARB: Advanced Reasoning Benchmark for Large Language Models. 2023. Publisher: arXiv Version Number: 2.
[15] Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language Agents with Verbal Reinforcement Learning, June 2023. arXiv:2303.11366 [cs].
[16] Michael Trick. Graph Coloring Instances. Available at https://mat.tepper.cmu.edu/ COLOR/instances.html.
|
2310.12397#28
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 28,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "[12] R. Thomas McCoy, Shunyu Yao, Dan Friedman, Matthew Hardy, and Thomas L. Griffiths. Embers of autoregression: Understanding large language models through the problem they are trained to solve, 2023.\n[13] Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach (4th Edition). Pearson, 2020.\n[14] Tomohiro Sawada, Daniel Paleka, Alexander Havrilla, Pranav Tadepalli, Paula Vidas, Alexander Kranias, John J. Nay, Kshitij Gupta, and Aran Komatsuzaki. ARB: Advanced Reasoning Benchmark for Large Language Models. 2023. Publisher: arXiv Version Number: 2.\n[15] Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language Agents with Verbal Reinforcement Learning, June 2023. arXiv:2303.11366 [cs].\n[16] Michael Trick. Graph Coloring Instances. Available at https://mat.tepper.cmu.edu/ COLOR/instances.html.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 28 |
However, evaluating large language models has consistently been a challenging and unresolved problem. Traditional benchmarks often do not capture the full extent to which a model aligns with human values. This shortcoming is largely attributable to inconsistent standards and unequivocal outcomes in human alignment evaluation. Thus, we prefer to assess large language models based on their responses to specific prompts. We employ two methods for overall assessment. These include a rapid evaluation of our models using our trained unified Reward Model and Cost Model; deriving the Elo score by comparing model outputs with human judgments and GPT-4 evaluations.
|
2310.12773#28
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 28,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "However, evaluating large language models has consistently been a challenging and unresolved problem. Traditional benchmarks often do not capture the full extent to which a model aligns with human values. This shortcoming is largely attributable to inconsistent standards and unequivocal outcomes in human alignment evaluation. Thus, we prefer to assess large language models based on their responses to specific prompts. We employ two methods for overall assessment. These include a rapid evaluation of our models using our trained unified Reward Model and Cost Model; deriving the Elo score by comparing model outputs with human judgments and GPT-4 evaluations.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 29 |
[16] Michael Trick. Graph Coloring Instances. Available at https://mat.tepper.cmu.edu/ COLOR/instances.html.
[17] Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. Large Language Models Still Canât Plan (A Benchmark for LLMs on Planning and Reasoning about Change), apr 2023. arXiv:2206.10498 [cs].
[18] Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Kang Liu, and Jun Zhao. Large language models are better reasoners with self-verification, 2023.
[19] Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models, 2023.
[20] Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models, 2023.
9
# A Appendix
# A.1 Prompts
All of following examples are built on the same graph instance.
|
2310.12397#29
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 29,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "[16] Michael Trick. Graph Coloring Instances. Available at https://mat.tepper.cmu.edu/ COLOR/instances.html.\n[17] Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. Large Language Models Still Canât Plan (A Benchmark for LLMs on Planning and Reasoning about Change), apr 2023. arXiv:2206.10498 [cs].\n[18] Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Kang Liu, and Jun Zhao. Large language models are better reasoners with self-verification, 2023.\n[19] Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models, 2023.\n[20] Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models, 2023.\n9\n# A Appendix\n# A.1 Prompts\nAll of following examples are built on the same graph instance.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 29 |
Model-based Evaluations. Despite human evaluation remaining the gold standard for aligning large language models with human values, the reliance on this method alone is neither practical nor efficient due to considerable associated time and financial costs. Such limitations necessitate alter- native assessment methods to complement human evaluation. Thus, we have developed a unified Reward Model and a unified Cost Model, utilizing training methodologies mentioned in Section 3.2. These models are trained on evenly balanced preference data originating from all iterations of Safe RLHF. With these unified models, we can rapidly evaluate subsequent new models under consistent criteria. The test accuracies for the unified models are detailed in Table 1. Note that we do not employ these unified models to train a single-round Safe RLHF process, as the preference data ac- quisition occurs iteratively. We need intermediate models for the red-teaming procedure, facilitating the collection of new prompts for the follow-up training phases.
As illustrated in Figure 4, our SFT model, the Alpaca-7B model (reproduced), has the ability to produce both harmless and harmful responses that are almost evenly separated on each side of the c = 0 dividing line (Figure 4a). Following the first round of Safe RLHF training, there is an
7
|
2310.12773#29
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 29,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "Model-based Evaluations. Despite human evaluation remaining the gold standard for aligning large language models with human values, the reliance on this method alone is neither practical nor efficient due to considerable associated time and financial costs. Such limitations necessitate alter- native assessment methods to complement human evaluation. Thus, we have developed a unified Reward Model and a unified Cost Model, utilizing training methodologies mentioned in Section 3.2. These models are trained on evenly balanced preference data originating from all iterations of Safe RLHF. With these unified models, we can rapidly evaluate subsequent new models under consistent criteria. The test accuracies for the unified models are detailed in Table 1. Note that we do not employ these unified models to train a single-round Safe RLHF process, as the preference data ac- quisition occurs iteratively. We need intermediate models for the red-teaming procedure, facilitating the collection of new prompts for the follow-up training phases.\nAs illustrated in Figure 4, our SFT model, the Alpaca-7B model (reproduced), has the ability to produce both harmless and harmful responses that are almost evenly separated on each side of the c = 0 dividing line (Figure 4a). Following the first round of Safe RLHF training, there is an\n7",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12773
| 30 |
7
Table 1: The test accuracy for the Reward Model and Cost Model for the three rounds of Safe RLHF training stages. The unified preference models are trained and tested on evenly balanced preference data from the preference dataset used in the three Safe RLHF iterations.
Model Reward Model Cost Model Metric Ranking Accuracy Ranking Accuracy Safety Classification Accuracy Beaver-v1 Beaver-v2 Beaver-v3 Unified 73.95% 70.44% 85.83% 78.13% 74.47% 95.62% 75.73% 76.07% 84.54% 77.32% 74.17% 85.88%
appreciable shift in the model response distribution towards the side with a lower cost, implying safer outputs (Figure 4b). During the second iteration of Safe RLHF, there is a decline in harmful content, denoted by the c > 0 region (Figure 4c). In the final iteration, the data cluster gravitates towards the higher reward direction, while successfully maintaining the majority of the responses as harmless (Figure 4d).
|
2310.12773#30
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 30,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "7\nTable 1: The test accuracy for the Reward Model and Cost Model for the three rounds of Safe RLHF training stages. The unified preference models are trained and tested on evenly balanced preference data from the preference dataset used in the three Safe RLHF iterations.\nModel Reward Model Cost Model Metric Ranking Accuracy Ranking Accuracy Safety Classification Accuracy Beaver-v1 Beaver-v2 Beaver-v3 Unified 73.95% 70.44% 85.83% 78.13% 74.47% 95.62% 75.73% 76.07% 84.54% 77.32% 74.17% 85.88%\nappreciable shift in the model response distribution towards the side with a lower cost, implying safer outputs (Figure 4b). During the second iteration of Safe RLHF, there is a decline in harmful content, denoted by the c > 0 region (Figure 4c). In the final iteration, the data cluster gravitates towards the higher reward direction, while successfully maintaining the majority of the responses as harmless (Figure 4d).",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 31 |
# Baseline, Direct Prompt
Color the following graph, described as a set of edges, such that no two vertices on the same edge <> share a color. You may use at most 3 colors. Vertex 0 is connected to vertex 7. Vertex 0 is connected to vertex 8. Vertex 0 is connected to vertex 9. Vertex 0 is connected to vertex 11. Vertex 1 is connected to vertex 13. Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex 10 is connected to vertex 13. Vertex 11 is connected to vertex 13. There are a total of 14 vertices. Please label every vertex, even if it is disconnected from the <> rest of the graph.Please provide each vertex's color. Do not skip any vertices. Each color <+ must be provided on a new line in the response and should be formatted as "{VERTEX NUMBER}: <> {VERTEX COLOR ASSIGNMENT}". Please do not provide anything else in your response.
|
2310.12397#31
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 31,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "# Baseline, Direct Prompt\nColor the following graph, described as a set of edges, such that no two vertices on the same edge <> share a color. You may use at most 3 colors. Vertex 0 is connected to vertex 7. Vertex 0 is connected to vertex 8. Vertex 0 is connected to vertex 9. Vertex 0 is connected to vertex 11. Vertex 1 is connected to vertex 13. Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex 10 is connected to vertex 13. Vertex 11 is connected to vertex 13. There are a total of 14 vertices. Please label every vertex, even if it is disconnected from the <> rest of the graph.Please provide each vertex's color. Do not skip any vertices. Each color <+ must be provided on a new line in the response and should be formatted as \"{VERTEX NUMBER}: <> {VERTEX COLOR ASSIGNMENT}\". Please do not provide anything else in your response.",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 31 |
GPT-4 and Human Evaluations. For more accurate assessments, we compare models against each other to generate associated Elo scores, as described in Askell et al. (2021). Specifically, evaluators compare the outputs of two models in response to the same prompt and provide their preferences regarding helpfulness and harmlessness. After obtaining pairwise win-rate relationships between all models, we fit corresponding Elo scores (with an initial score of 1200). According to Chiang & Lee (2023), GPT-4 can replace human evaluators in assessing the alignment capabilities of LLMs. Therefore, we have organized assessments involving both GPT-4 and human evaluators.
|
2310.12773#31
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 31,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "GPT-4 and Human Evaluations. For more accurate assessments, we compare models against each other to generate associated Elo scores, as described in Askell et al. (2021). Specifically, evaluators compare the outputs of two models in response to the same prompt and provide their preferences regarding helpfulness and harmlessness. After obtaining pairwise win-rate relationships between all models, we fit corresponding Elo scores (with an initial score of 1200). According to Chiang & Lee (2023), GPT-4 can replace human evaluators in assessing the alignment capabilities of LLMs. Therefore, we have organized assessments involving both GPT-4 and human evaluators.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 32 |
Example LLM Response 0: Color1 1: Color1 2: Color1 3: Color2 4: Color1 5: Color2 6: Color2 7: Color2 8: Color3 9: Color3 10: Color3 11: Color3 12: Color3 13: Color1
10
# Prompt To Elicit Verification
The following graph, described as a set of edges, has an optimal coloring number of 3: Vertex 0 is connected to vertex 7. Vertex 0 is connected to vertex 8. Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex 10 is connected to vertex 13. Vertex 11 is connected to vertex 13. Please check if this coloring is correct:0: Colori Colort Colort : Color2 Colort : Color2 Color2 : Color2 Color3 : Color3 : Color3 : Color3 : Color3 : Colori it is, say 'Verifier confirmed success.' Do not provide anything else in your response. If it is incorrect, please point out which same-color vertices share an edge.
# A.2 Backprompts
|
2310.12397#32
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 32,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "Example LLM Response 0: Color1 1: Color1 2: Color1 3: Color2 4: Color1 5: Color2 6: Color2 7: Color2 8: Color3 9: Color3 10: Color3 11: Color3 12: Color3 13: Color1 \n10\n# Prompt To Elicit Verification\nThe following graph, described as a set of edges, has an optimal coloring number of 3: Vertex 0 is connected to vertex 7. Vertex 0 is connected to vertex 8. Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex 10 is connected to vertex 13. Vertex 11 is connected to vertex 13. Please check if this coloring is correct:0: Colori Colort Colort : Color2 Colort : Color2 Color2 : Color2 Color3 : Color3 : Color3 : Color3 : Color3 : Colori it is, say 'Verifier confirmed success.' Do not provide anything else in your response. If it is incorrect, please point out which same-color vertices share an edge.\n# A.2 Backprompts",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 32 |
As shown in Figure 5a and 5b, the three rounds of Safe RLHF significantly improved the Elo scores in both helpfulness and harmlessness, as evaluated by both GPT-4 and human evaluators. When compared to Alpaca-7B, the Beaver-v3 model demonstrated an increase in the Elo score for helpful- ness (GPT-4: +244.91, Human: +363.86) and for harmlessness (GPT-4: +268.31, Human: +237.98). Comparatively, the evaluations by GPT-4 and human evaluators are almost consistent. Notably, start- ing from the second round, we initiated red teaming attacks to broaden the scope of safety-related prompts. This effectively aided in making the Safe RLHF training models more harmless. During the third round, since the model was sufficiently safe, Safe RLHF tended to prioritize maintaining the current harmlessness level over excessive optimization. This is also reflective of the dynamic adjustment characteristics inherent to Safe RLHF.
|
2310.12773#32
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
[
{
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{
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},
{
"id": "1707.06347"
},
{
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 32,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "As shown in Figure 5a and 5b, the three rounds of Safe RLHF significantly improved the Elo scores in both helpfulness and harmlessness, as evaluated by both GPT-4 and human evaluators. When compared to Alpaca-7B, the Beaver-v3 model demonstrated an increase in the Elo score for helpful- ness (GPT-4: +244.91, Human: +363.86) and for harmlessness (GPT-4: +268.31, Human: +237.98). Comparatively, the evaluations by GPT-4 and human evaluators are almost consistent. Notably, start- ing from the second round, we initiated red teaming attacks to broaden the scope of safety-related prompts. This effectively aided in making the Safe RLHF training models more harmless. During the third round, since the model was sufficiently safe, Safe RLHF tended to prioritize maintaining the current harmlessness level over excessive optimization. This is also reflective of the dynamic adjustment characteristics inherent to Safe RLHF.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 33 |
# A.2 Backprompts
Backprompt Generated From Self-Critique This is incorrect. Feedback: Vertices 0 and 3 share an edge and are both colored with Colori. Vertices 9 and 11 share an edge <>» and are both colored with Color3. Using this feedback, please try again. Please provide each vertex's color. Do not skip any <> vertices. Each color must be provided on a new line in the response and should be formatted as <> "{VERTEX NUMBER}: {VERTEX COLOR ASSIGNMENT}". Please do not provide anything else in your <> response.
# Backprompt Generated From Sound Verifier (Pass/Fail)
This is not correct. Using the previously provided graph, please provide a correct coloring. <> Please provide each vertex's color. Do not skip any vertices. Each color must be provided <> new line in the response and should be formatted as "{VERTEX NUMBER}: {VERTEX COLOR <> ASSIGNMENT}". Please do not provide anything else in your response.
Please provide each vertex's color. Do not skip any vertices. Each color must be provided on a new line in the response and should be formatted as "{VERTEX NUMBER}: {VERTEX COLOR ASSIGNMENT}". Please do not provide anything else in your response.
Backprompt Generated From Sound Verifier (First)
|
2310.12397#33
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 33,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "# A.2 Backprompts\nBackprompt Generated From Self-Critique This is incorrect. Feedback: Vertices 0 and 3 share an edge and are both colored with Colori. Vertices 9 and 11 share an edge <>» and are both colored with Color3. Using this feedback, please try again. Please provide each vertex's color. Do not skip any <> vertices. Each color must be provided on a new line in the response and should be formatted as <> \"{VERTEX NUMBER}: {VERTEX COLOR ASSIGNMENT}\". Please do not provide anything else in your <> response.\n# Backprompt Generated From Sound Verifier (Pass/Fail)\nThis is not correct. Using the previously provided graph, please provide a correct coloring. <> Please provide each vertex's color. Do not skip any vertices. Each color must be provided <> new line in the response and should be formatted as \"{VERTEX NUMBER}: {VERTEX COLOR <> ASSIGNMENT}\". Please do not provide anything else in your response.\nPlease provide each vertex's color. Do not skip any vertices. Each color must be provided on a new line in the response and should be formatted as \"{VERTEX NUMBER}: {VERTEX COLOR ASSIGNMENT}\". Please do not provide anything else in your response.\nBackprompt Generated From Sound Verifier (First)",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 33 |
Meanwhile, our crowdworkers also labeled whether the modelsâ responses are safe, as shown in Figure 5c. Through three rounds of Safe RLHF training, the Beaver-v3 modelâs probability of harmful responses on the evaluation set decreased from 53.08% for Alpaca-7B to 2.45%. For the specific prompts used in the GPT-4 evaluation, please refer to Appendix C.2.
4.2.2 THE DECOUPLING OF HARMLESSNESS AND HELPFULNESS
In this section, we aim to demonstrate the benefits of explicitly separating harmlessness and helpful- ness in the Safe RLHF pipeline. We use the responses collected from the first round of Safe RLHF to carry out preference labeling and PPO training following the conventional RLHF methodology. During the preference labeling, the difference is that only a comprehensive preference is provided, while other aspects align with Safe RLHF.
|
2310.12773#33
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
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{
"id": "2304.05302"
},
{
"id": "1707.06347"
},
{
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{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 33,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "Meanwhile, our crowdworkers also labeled whether the modelsâ responses are safe, as shown in Figure 5c. Through three rounds of Safe RLHF training, the Beaver-v3 modelâs probability of harmful responses on the evaluation set decreased from 53.08% for Alpaca-7B to 2.45%. For the specific prompts used in the GPT-4 evaluation, please refer to Appendix C.2.\n4.2.2 THE DECOUPLING OF HARMLESSNESS AND HELPFULNESS\nIn this section, we aim to demonstrate the benefits of explicitly separating harmlessness and helpful- ness in the Safe RLHF pipeline. We use the responses collected from the first round of Safe RLHF to carry out preference labeling and PPO training following the conventional RLHF methodology. During the preference labeling, the difference is that only a comprehensive preference is provided, while other aspects align with Safe RLHF.",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 34 |
Backprompt Generated From Sound Verifier (First)
Vertex 9 and vertex 11 were both colored Color3 despite being connected by an edge. This is wrong. Please recolor. Please provide each vertex's color. Do not skip any vertices. Each <+ color must be provided on a new line in the response and should be formatted as "{VERTEX <> NUMBER}: {VERTEX COLOR ASSIGNMENT}". Please do not provide anything else in your response.
11
Backprompt Generated From Sound Verifier (Full)
Vertex 9 and vertex 11 were both colored Color3 despite being connected by an edge. Vertex 10 and <> vertex 13 were both colored Color3 despite being connected by an edge. Vertex 11 and vertex <+ were both colored Color3 despite being connected by an edge. This is wrong. Please recolor. Please provide each vertex's color. Do not skip any vertices. Each <+ color must be provided on a new line in the response and should be formatted as "{VERTEX <> NUMBER}: {VERTEX COLOR ASSIGNMENT}". Please do not provide anything else in your response.
13
A.3 Complete Example of Iterative Prompt Sequence
Full Sequence of 15 (LLM-Generated) Backprompts
|
2310.12397#34
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 34,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "Backprompt Generated From Sound Verifier (First)\nVertex 9 and vertex 11 were both colored Color3 despite being connected by an edge. This is wrong. Please recolor. Please provide each vertex's color. Do not skip any vertices. Each <+ color must be provided on a new line in the response and should be formatted as \"{VERTEX <> NUMBER}: {VERTEX COLOR ASSIGNMENT}\". Please do not provide anything else in your response.\n11\nBackprompt Generated From Sound Verifier (Full)\nVertex 9 and vertex 11 were both colored Color3 despite being connected by an edge. Vertex 10 and <> vertex 13 were both colored Color3 despite being connected by an edge. Vertex 11 and vertex <+ were both colored Color3 despite being connected by an edge. This is wrong. Please recolor. Please provide each vertex's color. Do not skip any vertices. Each <+ color must be provided on a new line in the response and should be formatted as \"{VERTEX <> NUMBER}: {VERTEX COLOR ASSIGNMENT}\". Please do not provide anything else in your response.\n13\nA.3 Complete Example of Iterative Prompt Sequence\nFull Sequence of 15 (LLM-Generated) Backprompts",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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] |
2310.12773
| 34 |
Compared to single-dimensional annotation and training, we observe the following advantages of Safe RLHF: First, decoupling the annotations for helpfulness and harmlessness results in higher Inter-Rater Agreement Rate among crowdworkers, which is Helpfulness: 69.00% and Safety: 66.53% compared to 61.65%. Second, the agreement between crowdworkers and researchers (i.e. approval rate) is also increased. In single-dimensional annotation, the average approval rate dur- ing a 10% quality inspection drops from at least 90% accuracy to below 80%. Third, as shown in Figure 6a, using the above data for PPO training results in a notable improvement in helpfulness. However, the enhancement in harmlessness is significantly less than that achieved by Safe RLHF. In contrast, Safe RLHF allows a subjective adjustment in the training phase to balance helpfulness and harmlessness.
8
|
2310.12773#34
|
Safe RLHF: Safe Reinforcement Learning from Human Feedback
|
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
|
http://arxiv.org/pdf/2310.12773
|
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
|
cs.AI, cs.LG
| null | null |
cs.AI
|
20231019
|
20231019
|
[
{
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{
"id": "2304.05302"
},
{
"id": "1707.06347"
},
{
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{
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{
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{
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] |
{
"authors": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang",
"chunk_id": 34,
"doc_id": "2310.12773",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12773",
"summary": "With the development of large language models (LLMs), striking a balance\nbetween the performance and safety of AI systems has never been more critical.\nHowever, the inherent tension between the objectives of helpfulness and\nharmlessness presents a significant challenge during LLM training. To address\nthis issue, we propose Safe Reinforcement Learning from Human Feedback (Safe\nRLHF), a novel algorithm for human value alignment. Safe RLHF explicitly\ndecouples human preferences regarding helpfulness and harmlessness, effectively\navoiding the crowdworkers' confusion about the tension and allowing us to train\nseparate reward and cost models. We formalize the safety concern of LLMs as an\noptimization task of maximizing the reward function while satisfying specified\ncost constraints. Leveraging the Lagrangian method to solve this constrained\nproblem, Safe RLHF dynamically adjusts the balance between the two objectives\nduring fine-tuning. Through a three-round fine-tuning using Safe RLHF, we\ndemonstrate a superior ability to mitigate harmful responses while enhancing\nmodel performance compared to existing value-aligned algorithms.\nExperimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with\ncollected human preferences, significantly improving its helpfulness and\nharmlessness according to human evaluations.",
"text": "Compared to single-dimensional annotation and training, we observe the following advantages of Safe RLHF: First, decoupling the annotations for helpfulness and harmlessness results in higher Inter-Rater Agreement Rate among crowdworkers, which is Helpfulness: 69.00% and Safety: 66.53% compared to 61.65%. Second, the agreement between crowdworkers and researchers (i.e. approval rate) is also increased. In single-dimensional annotation, the average approval rate dur- ing a 10% quality inspection drops from at least 90% accuracy to below 80%. Third, as shown in Figure 6a, using the above data for PPO training results in a notable improvement in helpfulness. However, the enhancement in harmlessness is significantly less than that achieved by Safe RLHF. In contrast, Safe RLHF allows a subjective adjustment in the training phase to balance helpfulness and harmlessness.\n8",
"title": "Safe RLHF: Safe Reinforcement Learning from Human Feedback",
"year": 2023
}
|
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] |
2310.12397
| 35 |
Color the following graph, described as a set of edges, such that no two vertices on the same edge <> share a color. You may use at most 3 colors. Vertex 0 is connected to vertex 7. Vertex 0 is connected to vertex 8. Vertex 0 is connected to vertex 9. Vertex 0 is connected to vertex 11. Vertex 1 is connected to vertex 13. Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex 10 is connected to vertex 13. Vertex 11 is connected to vertex 13. There are a total of 14 vertices. Please label every vertex, even if it is disconnected from the <> rest of the graph.Please provide each vertex's color. Do not skip any vertices. Each color <+ must be provided on a new line in the response and should be formatted as "{VERTEX NUMBER}: <> {VERTEX COLOR ASSIGNMENT}". Please do not provide anything else in your response. 0: Color1 Colort : Colort Colort : Colort Color2 Color2 Color2 Color2 Color3 :
|
2310.12397#35
|
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
|
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automatically with scale has been tempered thanks to a slew of
counterexamples, a wide spread belief in their iterative self-critique
capabilities persists. In this paper, we set out to systematically investigate
the effectiveness of iterative prompting of LLMs in the context of Graph
Coloring, a canonical NP-complete reasoning problem that is related to
propositional satisfiability as well as practical problems like scheduling and
allocation. We present a principled empirical study of the performance of GPT4
in solving graph coloring instances or verifying the correctness of candidate
colorings. In iterative modes, we experiment with the model critiquing its own
answers and an external correct reasoner verifying proposed solutions. In both
cases, we analyze whether the content of the criticisms actually affects bottom
line performance. The study seems to indicate that (i) LLMs are bad at solving
graph coloring instances (ii) they are no better at verifying a solution--and
thus are not effective in iterative modes with LLMs critiquing LLM-generated
solutions (iii) the correctness and content of the criticisms--whether by LLMs
or external solvers--seems largely irrelevant to the performance of iterative
prompting. We show that the observed increase in effectiveness is largely due
to the correct solution being fortuitously present in the top-k completions of
the prompt (and being recognized as such by an external verifier). Our results
thus call into question claims about the self-critiquing capabilities of state
of the art LLMs.
|
http://arxiv.org/pdf/2310.12397
|
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
|
cs.AI
|
18 pages, 3 figures
| null |
cs.AI
|
20231019
|
20231019
|
[
{
"id": "2206.10498"
},
{
"id": "2306.03872"
},
{
"id": "2303.11366"
}
] |
{
"authors": "Kaya Stechly, Matthew Marquez, Subbarao Kambhampati",
"chunk_id": 35,
"doc_id": "2310.12397",
"primary_category": "cs.AI",
"published": 20231019,
"source": "http://arxiv.org/pdf/2310.12397",
"summary": "There has been considerable divergence of opinion on the reasoning abilities\nof Large Language Models (LLMs). While the initial optimism that reasoning\nmight emerge automatically with scale has been tempered thanks to a slew of\ncounterexamples, a wide spread belief in their iterative self-critique\ncapabilities persists. In this paper, we set out to systematically investigate\nthe effectiveness of iterative prompting of LLMs in the context of Graph\nColoring, a canonical NP-complete reasoning problem that is related to\npropositional satisfiability as well as practical problems like scheduling and\nallocation. We present a principled empirical study of the performance of GPT4\nin solving graph coloring instances or verifying the correctness of candidate\ncolorings. In iterative modes, we experiment with the model critiquing its own\nanswers and an external correct reasoner verifying proposed solutions. In both\ncases, we analyze whether the content of the criticisms actually affects bottom\nline performance. The study seems to indicate that (i) LLMs are bad at solving\ngraph coloring instances (ii) they are no better at verifying a solution--and\nthus are not effective in iterative modes with LLMs critiquing LLM-generated\nsolutions (iii) the correctness and content of the criticisms--whether by LLMs\nor external solvers--seems largely irrelevant to the performance of iterative\nprompting. We show that the observed increase in effectiveness is largely due\nto the correct solution being fortuitously present in the top-k completions of\nthe prompt (and being recognized as such by an external verifier). Our results\nthus call into question claims about the self-critiquing capabilities of state\nof the art LLMs.",
"text": "Color the following graph, described as a set of edges, such that no two vertices on the same edge <> share a color. You may use at most 3 colors. Vertex 0 is connected to vertex 7. Vertex 0 is connected to vertex 8. Vertex 0 is connected to vertex 9. Vertex 0 is connected to vertex 11. Vertex 1 is connected to vertex 13. Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex is connected to vertex Vertex 10 is connected to vertex 13. Vertex 11 is connected to vertex 13. There are a total of 14 vertices. Please label every vertex, even if it is disconnected from the <> rest of the graph.Please provide each vertex's color. Do not skip any vertices. Each color <+ must be provided on a new line in the response and should be formatted as \"{VERTEX NUMBER}: <> {VERTEX COLOR ASSIGNMENT}\". Please do not provide anything else in your response. 0: Color1 Colort : Colort Colort : Colort Color2 Color2 Color2 Color2 Color3 :",
"title": "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems",
"year": 2023
}
|
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