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2023-09-18T00:00:00 | 2309.08532 | Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers | [
"Qingyan Guo",
"Rui Wang",
"Junliang Guo",
"Bei Li",
"Kaitao Song",
"Xu Tan",
"Guoqing Liu",
"Jiang Bian",
"Yujiu Yang"
] | Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort. To automate this process, in this paper, we propose a novel framework for discrete prompt optimization, called EvoPrompt, which borrows the idea of evolutionary algorithms (EAs) as they exhibit good performance and fast convergence. To enable EAs to work on discrete prompts, which are natural language expressions that need to be coherent and human-readable, we connect LLMs with EAs. This approach allows us to simultaneously leverage the powerful language processing capabilities of LLMs and the efficient optimization performance of EAs. Specifically, abstaining from any gradients or parameters, EvoPrompt starts from a population of prompts and iteratively generates new prompts with LLMs based on the evolutionary operators, improving the population based on the development set. We optimize prompts for both closed- and open-source LLMs including GPT-3.5 and Alpaca, on 9 datasets spanning language understanding and generation tasks. EvoPrompt significantly outperforms human-engineered prompts and existing methods for automatic prompt generation by up to 25% and 14% respectively. Furthermore, EvoPrompt demonstrates that connecting LLMs with EAs creates synergies, which could inspire further research on the combination of LLMs and conventional algorithms. |
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2023-09-18T00:00:00 | 2309.08586 | Replacing softmax with ReLU in Vision Transformers | [
"Mitchell Wortsman",
"Jaehoon Lee",
"Justin Gilmer",
"Simon Kornblith"
] | Previous research observed accuracy degradation when replacing the attention softmax with a point-wise activation such as ReLU. In the context of vision transformers, we find that this degradation is mitigated when dividing by sequence length. Our experiments training small to large vision transformers on ImageNet-21k indicate that ReLU-attention can approach or match the performance of softmax-attention in terms of scaling behavior as a function of compute. |
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2023-09-18T00:00:00 | 2309.08210 | Investigating Answerability of LLMs for Long-Form Question Answering | [
"Meghana Moorthy Bhat",
"Rui Meng",
"Ye Liu",
"Yingbo Zhou",
"Semih Yavuz"
] | As we embark on a new era of LLMs, it becomes increasingly crucial to understand their capabilities, limitations, and differences. Toward making further progress in this direction, we strive to build a deeper understanding of the gaps between massive LLMs (e.g., ChatGPT) and smaller yet effective open-source LLMs and their distilled counterparts. To this end, we specifically focus on long-form question answering (LFQA) because it has several practical and impactful applications (e.g., troubleshooting, customer service, etc.) yet is still understudied and challenging for LLMs. We propose a question-generation method from abstractive summaries and show that generating follow-up questions from summaries of long documents can create a challenging setting for LLMs to reason and infer from long contexts. Our experimental results confirm that: (1) our proposed method of generating questions from abstractive summaries pose a challenging setup for LLMs and shows performance gaps between LLMs like ChatGPT and open-source LLMs (Alpaca, Llama) (2) open-source LLMs exhibit decreased reliance on context for generated questions from the original document, but their generation capabilities drop significantly on generated questions from summaries -- especially for longer contexts (>1024 tokens) |
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2023-09-18T00:00:00 | 2309.07970 | Language Embedded Radiance Fields for Zero-Shot Task-Oriented Grasping | [
"Adam Rashid",
"Satvik Sharma",
"Chung Min Kim",
"Justin Kerr",
"Lawrence Chen",
"Angjoo Kanazawa",
"Ken Goldberg"
] | Grasping objects by a specific part is often crucial for safety and for executing downstream tasks. Yet, learning-based grasp planners lack this behavior unless they are trained on specific object part data, making it a significant challenge to scale object diversity. Instead, we propose LERF-TOGO, Language Embedded Radiance Fields for Task-Oriented Grasping of Objects, which uses vision-language models zero-shot to output a grasp distribution over an object given a natural language query. To accomplish this, we first reconstruct a LERF of the scene, which distills CLIP embeddings into a multi-scale 3D language field queryable with text. However, LERF has no sense of objectness, meaning its relevancy outputs often return incomplete activations over an object which are insufficient for subsequent part queries. LERF-TOGO mitigates this lack of spatial grouping by extracting a 3D object mask via DINO features and then conditionally querying LERF on this mask to obtain a semantic distribution over the object with which to rank grasps from an off-the-shelf grasp planner. We evaluate LERF-TOGO's ability to grasp task-oriented object parts on 31 different physical objects, and find it selects grasps on the correct part in 81% of all trials and grasps successfully in 69%. See the project website at: lerftogo.github.io |
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2023-09-18T00:00:00 | 2309.08520 | Scaling Laws for Sparsely-Connected Foundation Models | [
"Elias Frantar",
"Carlos Riquelme",
"Neil Houlsby",
"Dan Alistarh",
"Utku Evci"
] | We explore the impact of parameter sparsity on the scaling behavior of Transformers trained on massive datasets (i.e., "foundation models"), in both vision and language domains. In this setting, we identify the first scaling law describing the relationship between weight sparsity, number of non-zero parameters, and amount of training data, which we validate empirically across model and data scales; on ViT/JFT-4B and T5/C4. These results allow us to characterize the "optimal sparsity", the sparsity level which yields the best performance for a given effective model size and training budget. For a fixed number of non-zero parameters, we identify that the optimal sparsity increases with the amount of data used for training. We also extend our study to different sparsity structures (such as the hardware-friendly n:m pattern) and strategies (such as starting from a pretrained dense model). Our findings shed light on the power and limitations of weight sparsity across various parameter and computational settings, offering both theoretical understanding and practical implications for leveraging sparsity towards computational efficiency improvements. |
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2023-09-18T00:00:00 | 2309.08600 | Sparse Autoencoders Find Highly Interpretable Features in Language Models | [
"Hoagy Cunningham",
"Aidan Ewart",
"Logan Riggs",
"Robert Huben",
"Lee Sharkey"
] | One of the roadblocks to a better understanding of neural networks' internals is polysemanticity, where neurons appear to activate in multiple, semantically distinct contexts. Polysemanticity prevents us from identifying concise, human-understandable explanations for what neural networks are doing internally. One hypothesised cause of polysemanticity is superposition, where neural networks represent more features than they have neurons by assigning features to an overcomplete set of directions in activation space, rather than to individual neurons. Here, we attempt to identify those directions, using sparse autoencoders to reconstruct the internal activations of a language model. These autoencoders learn sets of sparsely activating features that are more interpretable and monosemantic than directions identified by alternative approaches, where interpretability is measured by automated methods. Ablating these features enables precise model editing, for example, by removing capabilities such as pronoun prediction, while disrupting model behaviour less than prior techniques. This work indicates that it is possible to resolve superposition in language models using a scalable, unsupervised method. Our method may serve as a foundation for future mechanistic interpretability work, which we hope will enable greater model transparency and steerability. |
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2023-09-18T00:00:00 | 2309.08587 | Compositional Foundation Models for Hierarchical Planning | [
"Anurag Ajay",
"Seungwook Han",
"Yilun Du",
"Shaung Li",
"Abhi Gupta",
"Tommi Jaakkola",
"Josh Tenenbaum",
"Leslie Kaelbling",
"Akash Srivastava",
"Pulkit Agrawal"
] | To make effective decisions in novel environments with long-horizon goals, it is crucial to engage in hierarchical reasoning across spatial and temporal scales. This entails planning abstract subgoal sequences, visually reasoning about the underlying plans, and executing actions in accordance with the devised plan through visual-motor control. We propose Compositional Foundation Models for Hierarchical Planning (HiP), a foundation model which leverages multiple expert foundation model trained on language, vision and action data individually jointly together to solve long-horizon tasks. We use a large language model to construct symbolic plans that are grounded in the environment through a large video diffusion model. Generated video plans are then grounded to visual-motor control, through an inverse dynamics model that infers actions from generated videos. To enable effective reasoning within this hierarchy, we enforce consistency between the models via iterative refinement. We illustrate the efficacy and adaptability of our approach in three different long-horizon table-top manipulation tasks. |
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2023-09-18T00:00:00 | 2309.08172 | LASER: LLM Agent with State-Space Exploration for Web Navigation | [
"Kaixin Ma",
"Hongming Zhang",
"Hongwei Wang",
"Xiaoman Pan",
"Dong Yu"
] | Large language models (LLMs) have been successfully adapted for interactive decision-making tasks like web navigation. While achieving decent performance, previous methods implicitly assume a forward-only execution mode for the model, where they only provide oracle trajectories as in-context examples to teach the model how to reason in the interactive environment. Consequently, the model could not handle more challenging scenarios not covered in the in-context examples, e.g., mistakes, leading to sub-optimal performance. To address this issue, we propose to model the interactive task as state space exploration, where the LLM agent transitions among a pre-defined set of states by performing actions to complete the task. This formulation enables flexible back-tracking, allowing the model to easily recover from errors. We evaluate our proposed LLM Agent with State-Space ExploRation (LASER) on the WebShop task. Experimental results show that our LASER agent significantly outperforms previous methods and closes the gap with human performance on the web navigation task. |
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2023-09-18T00:00:00 | 2309.07990 | Leveraging Contextual Information for Effective Entity Salience Detection | [
"Rajarshi Bhowmik",
"Marco Ponza",
"Atharva Tendle",
"Anant Gupta",
"Rebecca Jiang",
"Xingyu Lu",
"Qian Zhao",
"Daniel Preotiuc-Pietro"
] | In text documents such as news articles, the content and key events usually revolve around a subset of all the entities mentioned in a document. These entities, often deemed as salient entities, provide useful cues of the aboutness of a document to a reader. Identifying the salience of entities was found helpful in several downstream applications such as search, ranking, and entity-centric summarization, among others. Prior work on salient entity detection mainly focused on machine learning models that require heavy feature engineering. We show that fine-tuning medium-sized language models with a cross-encoder style architecture yields substantial performance gains over feature engineering approaches. To this end, we conduct a comprehensive benchmarking of four publicly available datasets using models representative of the medium-sized pre-trained language model family. Additionally, we show that zero-shot prompting of instruction-tuned language models yields inferior results, indicating the task's uniqueness and complexity. |
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2023-09-18T00:00:00 | 2309.07974 | A Data Source for Reasoning Embodied Agents | [
"Jack Lanchantin",
"Sainbayar Sukhbaatar",
"Gabriel Synnaeve",
"Yuxuan Sun",
"Kavya Srinet",
"Arthur Szlam"
] | Recent progress in using machine learning models for reasoning tasks has been driven by novel model architectures, large-scale pre-training protocols, and dedicated reasoning datasets for fine-tuning. In this work, to further pursue these advances, we introduce a new data generator for machine reasoning that integrates with an embodied agent. The generated data consists of templated text queries and answers, matched with world-states encoded into a database. The world-states are a result of both world dynamics and the actions of the agent. We show the results of several baseline models on instantiations of train sets. These include pre-trained language models fine-tuned on a text-formatted representation of the database, and graph-structured Transformers operating on a knowledge-graph representation of the database. We find that these models can answer some questions about the world-state, but struggle with others. These results hint at new research directions in designing neural reasoning models and database representations. Code to generate the data will be released at github.com/facebookresearch/neuralmemory |
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2023-09-18T00:00:00 | 2309.08051 | Retrieval-Augmented Text-to-Audio Generation | [
"Yi Yuan",
"Haohe Liu",
"Xubo Liu",
"Qiushi Huang",
"Mark D. Plumbley",
"Wenwu Wang"
] | Despite recent progress in text-to-audio (TTA) generation, we show that the state-of-the-art models, such as AudioLDM, trained on datasets with an imbalanced class distribution, such as AudioCaps, are biased in their generation performance. Specifically, they excel in generating common audio classes while underperforming in the rare ones, thus degrading the overall generation performance. We refer to this problem as long-tailed text-to-audio generation. To address this issue, we propose a simple retrieval-augmented approach for TTA models. Specifically, given an input text prompt, we first leverage a Contrastive Language Audio Pretraining (CLAP) model to retrieve relevant text-audio pairs. The features of the retrieved audio-text data are then used as additional conditions to guide the learning of TTA models. We enhance AudioLDM with our proposed approach and denote the resulting augmented system as Re-AudioLDM. On the AudioCaps dataset, Re-AudioLDM achieves a state-of-the-art Frechet Audio Distance (FAD) of 1.37, outperforming the existing approaches by a large margin. Furthermore, we show that Re-AudioLDM can generate realistic audio for complex scenes, rare audio classes, and even unseen audio types, indicating its potential in TTA tasks. |
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2023-09-18T00:00:00 | 2309.07986 | Viewpoint Textual Inversion: Unleashing Novel View Synthesis with Pretrained 2D Diffusion Models | [
"James Burgess",
"Kuan-Chieh Wang",
"Serena Yeung"
] | Text-to-image diffusion models understand spatial relationship between objects, but do they represent the true 3D structure of the world from only 2D supervision? We demonstrate that yes, 3D knowledge is encoded in 2D image diffusion models like Stable Diffusion, and we show that this structure can be exploited for 3D vision tasks. Our method, Viewpoint Neural Textual Inversion (ViewNeTI), controls the 3D viewpoint of objects in generated images from frozen diffusion models. We train a small neural mapper to take camera viewpoint parameters and predict text encoder latents; the latents then condition the diffusion generation process to produce images with the desired camera viewpoint. ViewNeTI naturally addresses Novel View Synthesis (NVS). By leveraging the frozen diffusion model as a prior, we can solve NVS with very few input views; we can even do single-view novel view synthesis. Our single-view NVS predictions have good semantic details and photorealism compared to prior methods. Our approach is well suited for modeling the uncertainty inherent in sparse 3D vision problems because it can efficiently generate diverse samples. Our view-control mechanism is general, and can even change the camera view in images generated by user-defined prompts. |
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2023-09-19T00:00:00 | 2309.09958 | An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models | [
"Yadong Lu",
"Chunyuan Li",
"Haotian Liu",
"Jianwei Yang",
"Jianfeng Gao",
"Yelong Shen"
] | Visual instruction tuning has recently shown encouraging progress with open-source large multimodal models (LMM) such as LLaVA and MiniGPT-4. However, most existing studies of open-source LMM are performed using models with 13B parameters or smaller. In this paper we present an empirical study of scaling LLaVA up to 33B and 65B/70B, and share our findings from our explorations in image resolution, data mixing and parameter-efficient training methods such as LoRA/QLoRA. These are evaluated by their impact on the multi-modal and language capabilities when completing real-world tasks in the wild. We find that scaling LMM consistently enhances model performance and improves language capabilities, and performance of LoRA/QLoRA tuning of LMM are comparable to the performance of full-model fine-tuning. Additionally, the study highlights the importance of higher image resolutions and mixing multimodal-language data to improve LMM performance, and visual instruction tuning can sometimes improve LMM's pure language capability. We hope that this study makes state-of-the-art LMM research at a larger scale more accessible, thus helping establish stronger baselines for future research. Code and checkpoints will be made public. |
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2023-09-19T00:00:00 | 2309.09506 | LayoutNUWA: Revealing the Hidden Layout Expertise of Large Language Models | [
"Zecheng Tang",
"Chenfei Wu",
"Juntao Li",
"Nan Duan"
] | https://github.com/ProjectNUWA/LayoutNUWA | Graphic layout generation, a growing research field, plays a significant role in user engagement and information perception. Existing methods primarily treat layout generation as a numerical optimization task, focusing on quantitative aspects while overlooking the semantic information of layout, such as the relationship between each layout element. In this paper, we propose LayoutNUWA, the first model that treats layout generation as a code generation task to enhance semantic information and harness the hidden layout expertise of large language models~(LLMs). More concretely, we develop a Code Instruct Tuning (CIT) approach comprising three interconnected modules: 1) the Code Initialization (CI) module quantifies the numerical conditions and initializes them as HTML code with strategically placed masks; 2) the Code Completion (CC) module employs the formatting knowledge of LLMs to fill in the masked portions within the HTML code; 3) the Code Rendering (CR) module transforms the completed code into the final layout output, ensuring a highly interpretable and transparent layout generation procedure that directly maps code to a visualized layout. We attain significant state-of-the-art performance (even over 50\% improvements) on multiple datasets, showcasing the strong capabilities of LayoutNUWA. Our code is available at https://github.com/ProjectNUWA/LayoutNUWA. |
2023-09-19T00:00:00 | 2309.09971 | MindAgent: Emergent Gaming Interaction | [
"Ran Gong",
"Qiuyuan Huang",
"Xiaojian Ma",
"Hoi Vo",
"Zane Durante",
"Yusuke Noda",
"Zilong Zheng",
"Song-Chun Zhu",
"Demetri Terzopoulos",
"Li Fei-Fei",
"Jianfeng Gao"
] | Large Language Models (LLMs) have the capacity of performing complex scheduling in a multi-agent system and can coordinate these agents into completing sophisticated tasks that require extensive collaboration. However, despite the introduction of numerous gaming frameworks, the community has insufficient benchmarks towards building general multi-agents collaboration infrastructure that encompass both LLM and human-NPCs collaborations. In this work, we propose a novel infrastructure - MindAgent - to evaluate planning and coordination emergent capabilities for gaming interaction. In particular, our infrastructure leverages existing gaming framework, to i) require understanding of the coordinator for a multi-agent system, ii) collaborate with human players via un-finetuned proper instructions, and iii) establish an in-context learning on few-shot prompt with feedback. Furthermore, we introduce CUISINEWORLD, a new gaming scenario and related benchmark that dispatch a multi-agent collaboration efficiency and supervise multiple agents playing the game simultaneously. We conduct comprehensive evaluations with new auto-metric CoS for calculating the collaboration efficiency. Finally, our infrastructure can be deployed into real-world gaming scenarios in a customized VR version of CUISINEWORLD and adapted in existing broader Minecraft gaming domain. We hope our findings on LLMs and the new infrastructure for general-purpose scheduling and coordination can help shed light on how such skills can be obtained by learning from large language corpora. |
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2023-09-19T00:00:00 | 2309.09117 | Contrastive Decoding Improves Reasoning in Large Language Models | [
"Sean O'Brien",
"Mike Lewis"
] | We demonstrate that Contrastive Decoding -- a simple, computationally light, and training-free text generation method proposed by Li et al 2022 -- achieves large out-of-the-box improvements over greedy decoding on a variety of reasoning tasks. Originally shown to improve the perceived quality of long-form text generation, Contrastive Decoding searches for strings that maximize a weighted difference in likelihood between strong and weak models. We show that Contrastive Decoding leads LLaMA-65B to outperform LLaMA 2, GPT-3.5 and PaLM 2-L on the HellaSwag commonsense reasoning benchmark, and to outperform LLaMA 2, GPT-3.5 and PaLM-540B on the GSM8K math word reasoning benchmark, in addition to improvements on a collection of other tasks. Analysis suggests that Contrastive Decoding improves over existing methods by preventing some abstract reasoning errors, as well as by avoiding simpler modes such as copying sections of the input during chain-of-thought. Overall, Contrastive Decoding outperforms nucleus sampling for long-form generation and greedy decoding for reasoning tasks, making it a powerful general purpose method for generating text from language models. |
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2023-09-19T00:00:00 | 2309.08872 | PDFTriage: Question Answering over Long, Structured Documents | [
"Jon Saad-Falcon",
"Joe Barrow",
"Alexa Siu",
"Ani Nenkova",
"Ryan A. Rossi",
"Franck Dernoncourt"
] | Large Language Models (LLMs) have issues with document question answering (QA) in situations where the document is unable to fit in the small context length of an LLM. To overcome this issue, most existing works focus on retrieving the relevant context from the document, representing them as plain text. However, documents such as PDFs, web pages, and presentations are naturally structured with different pages, tables, sections, and so on. Representing such structured documents as plain text is incongruous with the user's mental model of these documents with rich structure. When a system has to query the document for context, this incongruity is brought to the fore, and seemingly trivial questions can trip up the QA system. To bridge this fundamental gap in handling structured documents, we propose an approach called PDFTriage that enables models to retrieve the context based on either structure or content. Our experiments demonstrate the effectiveness of the proposed PDFTriage-augmented models across several classes of questions where existing retrieval-augmented LLMs fail. To facilitate further research on this fundamental problem, we release our benchmark dataset consisting of 900+ human-generated questions over 80 structured documents from 10 different categories of question types for document QA. |
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2023-09-19T00:00:00 | 2309.08804 | Stack-and-Delay: a new codebook pattern for music generation | [
"Gael Le Lan",
"Varun Nagaraja",
"Ernie Chang",
"David Kant",
"Zhaoheng Ni",
"Yangyang Shi",
"Forrest Iandola",
"Vikas Chandra"
] | In language modeling based music generation, a generated waveform is represented by a sequence of hierarchical token stacks that can be decoded either in an auto-regressive manner or in parallel, depending on the codebook patterns. In particular, flattening the codebooks represents the highest quality decoding strategy, while being notoriously slow. To this end, we propose a novel stack-and-delay style of decoding strategy to improve upon the flat pattern decoding where generation speed is four times faster as opposed to vanilla flat decoding. This brings the inference time close to that of the delay decoding strategy, and allows for faster inference on GPU for small batch sizes. For the same inference efficiency budget as the delay pattern, we show that the proposed approach performs better in objective evaluations, almost closing the gap with the flat pattern in terms of quality. The results are corroborated by subjective evaluations which show that samples generated by the new model are slightly more often preferred to samples generated by the competing model given the same text prompts. |
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2023-09-19T00:00:00 | 2309.09530 | Adapting Large Language Models via Reading Comprehension | [
"Daixuan Cheng",
"Shaohan Huang",
"Furu Wei"
] | https://github.com/microsoft/LMOps | We explore how continued pre-training on domain-specific corpora influences large language models, revealing that training on the raw corpora endows the model with domain knowledge, but drastically hurts its prompting ability for question answering. Taken inspiration from human learning via reading comprehension--practice after reading improves the ability to answer questions based on the learned knowledge--we propose a simple method for transforming raw corpora into reading comprehension texts. Each raw text is enriched with a series of tasks related to its content. Our method, highly scalable and applicable to any pre-training corpora, consistently enhances performance across various tasks in three different domains: biomedicine, finance, and law. Notably, our 7B language model achieves competitive performance with domain-specific models of much larger scales, such as BloombergGPT-50B. Furthermore, we demonstrate that domain-specific reading comprehension texts can improve the model's performance even on general benchmarks, showing the potential to develop a general model across even more domains. Our model, code, and data will be available at https://github.com/microsoft/LMOps. |
2023-09-19T00:00:00 | 2309.09400 | CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages | [
"Thuat Nguyen",
"Chien Van Nguyen",
"Viet Dac Lai",
"Hieu Man",
"Nghia Trung Ngo",
"Franck Dernoncourt",
"Ryan A. Rossi",
"Thien Huu Nguyen"
] | The driving factors behind the development of large language models (LLMs) with impressive learning capabilities are their colossal model sizes and extensive training datasets. Along with the progress in natural language processing, LLMs have been frequently made accessible to the public to foster deeper investigation and applications. However, when it comes to training datasets for these LLMs, especially the recent state-of-the-art models, they are often not fully disclosed. Creating training data for high-performing LLMs involves extensive cleaning and deduplication to ensure the necessary level of quality. The lack of transparency for training data has thus hampered research on attributing and addressing hallucination and bias issues in LLMs, hindering replication efforts and further advancements in the community. These challenges become even more pronounced in multilingual learning scenarios, where the available multilingual text datasets are often inadequately collected and cleaned. Consequently, there is a lack of open-source and readily usable dataset to effectively train LLMs in multiple languages. To overcome this issue, we present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for LLM development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. CulturaX is fully released to the public in HuggingFace to facilitate research and advancements in multilingual LLMs: https://huggingface.co/datasets/uonlp/CulturaX. |
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2023-09-19T00:00:00 | 2309.09390 | Augmenting text for spoken language understanding with Large Language Models | [
"Roshan Sharma",
"Suyoun Kim",
"Daniel Lazar",
"Trang Le",
"Akshat Shrivastava",
"Kwanghoon Ahn",
"Piyush Kansal",
"Leda Sari",
"Ozlem Kalinli",
"Michael Seltzer"
] | Spoken semantic parsing (SSP) involves generating machine-comprehensible parses from input speech. Training robust models for existing application domains represented in training data or extending to new domains requires corresponding triplets of speech-transcript-semantic parse data, which is expensive to obtain. In this paper, we address this challenge by examining methods that can use transcript-semantic parse data (unpaired text) without corresponding speech. First, when unpaired text is drawn from existing textual corpora, Joint Audio Text (JAT) and Text-to-Speech (TTS) are compared as ways to generate speech representations for unpaired text. Experiments on the STOP dataset show that unpaired text from existing and new domains improves performance by 2% and 30% in absolute Exact Match (EM) respectively. Second, we consider the setting when unpaired text is not available in existing textual corpora. We propose to prompt Large Language Models (LLMs) to generate unpaired text for existing and new domains. Experiments show that examples and words that co-occur with intents can be used to generate unpaired text with Llama 2.0. Using the generated text with JAT and TTS for spoken semantic parsing improves EM on STOP by 1.4% and 2.6% absolute for existing and new domains respectively. |
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2023-09-19T00:00:00 | 2309.08968 | Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference Using Sorted Fine-Tuning (SoFT) | [
"Parsa Kavehzadeh",
"Mojtaba Valipour",
"Marzieh Tahaei",
"Ali Ghodsi",
"Boxing Chen",
"Mehdi Rezagholizadeh"
] | The rapid advancement of large language models (LLMs) has revolutionized natural language processing (NLP). While these models excel at understanding and generating human-like text, their widespread deployment can be prohibitively expensive. SortedNet is a recent training technique for enabling dynamic inference for deep neural networks. It leverages network modularity to create sub-models with varying computational loads, sorting them based on computation/accuracy characteristics in a nested manner. We extend SortedNet to generative NLP tasks, making large language models dynamic without any pretraining and by only replacing standard Supervised Fine-Tuning (SFT) with Sorted Fine-Tuning (SoFT) at the same costs. Our approach boosts model efficiency, eliminating the need for multiple models for various scenarios during inference. We show that using this approach, we are able to unlock the potential of intermediate layers of transformers in generating the target output. Our sub-models remain integral components of the original model, minimizing storage requirements and transition costs between different computational/latency budgets. By applying this approach on LLaMa 2 13B for tuning on the Stanford Alpaca dataset and comparing it to normal tuning and early exit via PandaLM benchmark, we show that Sorted Fine-Tuning can deliver models twice as fast as the original model while maintaining or exceeding performance. |
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2023-09-19T00:00:00 | 2309.08963 | Struc-Bench: Are Large Language Models Really Good at Generating Complex Structured Data? | [
"Xiangru Tang",
"Yiming Zong",
"Yilun Zhao",
"Arman Cohan",
"Mark Gerstein"
] | https://github.com/gersteinlab/Struc-Bench | Despite the power of Large Language Models (LLMs) like GPT-4, they still struggle with tasks that require generating complex, structured outputs. In this study, we assess the capability of Current LLMs in generating complex structured data and propose a structure-aware fine-tuning approach as a solution to improve this ability. To perform a comprehensive evaluation, we propose Struc-Bench, include five representative LLMs (i.e., GPT-NeoX 20B, GPT-3.5, GPT-4, and Vicuna) and evaluate them on our carefully constructed datasets spanning raw text, HTML, and LaTeX tables. Based on our analysis of current model performance, we identify specific common formatting errors and areas of potential improvement. To address complex formatting requirements, we utilize FormatCoT (Chain-of-Thought) to generate format instructions from target outputs. Our experiments show that our structure-aware fine-tuning method, when applied to LLaMA-7B, significantly improves adherence to natural language constraints, outperforming other evaluated LLMs. Based on these results, we present an ability map of model capabilities from six dimensions (i.e., coverage, formatting, reasoning, comprehension, pragmatics, and hallucination). This map highlights the weaknesses of LLMs in handling complex structured outputs and suggests promising directions for future work. Our code and models can be found at https://github.com/gersteinlab/Struc-Bench. |
2023-09-19T00:00:00 | 2309.08827 | S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs | [
"Sarkar Snigdha Sarathi Das",
"Chirag Shah",
"Mengting Wan",
"Jennifer Neville",
"Longqi Yang",
"Reid Andersen",
"Georg Buscher",
"Tara Safavi"
] | The traditional Dialogue State Tracking (DST) problem aims to track user preferences and intents in user-agent conversations. While sufficient for task-oriented dialogue systems supporting narrow domain applications, the advent of Large Language Model (LLM)-based chat systems has introduced many real-world intricacies in open-domain dialogues. These intricacies manifest in the form of increased complexity in contextual interactions, extended dialogue sessions encompassing a diverse array of topics, and more frequent contextual shifts. To handle these intricacies arising from evolving LLM-based chat systems, we propose joint dialogue segmentation and state tracking per segment in open-domain dialogue systems. Assuming a zero-shot setting appropriate to a true open-domain dialogue system, we propose S3-DST, a structured prompting technique that harnesses Pre-Analytical Recollection, a novel grounding mechanism we designed for improving long context tracking. To demonstrate the efficacy of our proposed approach in joint segmentation and state tracking, we evaluate S3-DST on a proprietary anonymized open-domain dialogue dataset, as well as publicly available DST and segmentation datasets. Across all datasets and settings, S3-DST consistently outperforms the state-of-the-art, demonstrating its potency and robustness the next generation of LLM-based chat systems. |
|
2023-09-19T00:00:00 | 2309.08773 | Enhance audio generation controllability through representation similarity regularization | [
"Yangyang Shi",
"Gael Le Lan",
"Varun Nagaraja",
"Zhaoheng Ni",
"Xinhao Mei",
"Ernie Chang",
"Forrest Iandola",
"Yang Liu",
"Vikas Chandra"
] | This paper presents an innovative approach to enhance control over audio generation by emphasizing the alignment between audio and text representations during model training. In the context of language model-based audio generation, the model leverages input from both textual and audio token representations to predict subsequent audio tokens. However, the current configuration lacks explicit regularization to ensure the alignment between the chosen text representation and the language model's predictions. Our proposal involves the incorporation of audio and text representation regularization, particularly during the classifier-free guidance (CFG) phase, where the text condition is excluded from cross attention during language model training. The aim of this proposed representation regularization is to minimize discrepancies in audio and text similarity compared to other samples within the same training batch. Experimental results on both music and audio generation tasks demonstrate that our proposed methods lead to improvements in objective metrics for both audio and music generation, as well as an enhancement in the human perception for audio generation. |
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2023-09-19T00:00:00 | 2309.08646 | Cure the headache of Transformers via Collinear Constrained Attention | [
"Shiyi Zhu",
"Jing Ye",
"Wei Jiang",
"Qi Zhang",
"Yifan Wu",
"Jianguo Li"
] | As the rapid progression of practical applications based on Large Language Models continues, the importance of extrapolating performance has grown exponentially in the research domain. In our study, we identified an anomalous behavior in Transformer models that had been previously overlooked, leading to a chaos around closest tokens which carried the most important information. We've coined this discovery the "headache of Transformers". To address this at its core, we introduced a novel self-attention structure named Collinear Constrained Attention (CoCA). This structure can be seamlessly integrated with existing extrapolation, interpolation methods, and other optimization strategies designed for traditional Transformer models. We have achieved excellent extrapolating performance even for 16 times to 24 times of sequence lengths during inference without any fine-tuning on our model. We have also enhanced CoCA's computational and spatial efficiency to ensure its practicality. We plan to open-source CoCA shortly. In the meantime, we've made our code available in the appendix for reappearing experiments. |
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2023-09-19T00:00:00 | 2309.08637 | TextBind: Multi-turn Interleaved Multimodal Instruction-following | [
"Huayang Li",
"Siheng Li",
"Deng Cai",
"Longyue Wang",
"Lemao Liu",
"Taro Watanabe",
"Yujiu Yang",
"Shuming Shi"
] | Large language models with instruction-following abilities have revolutionized the field of artificial intelligence. These models show exceptional generalizability to tackle various real-world tasks through their natural language interfaces. However, their performance heavily relies on high-quality exemplar data, which is often difficult to obtain. This challenge is further exacerbated when it comes to multimodal instruction following. We introduce TextBind, an almost annotation-free framework for empowering larger language models with the multi-turn interleaved multimodal instruction-following capabilities. Our approach requires only image-caption pairs and generates multi-turn multimodal instruction-response conversations from a language model. We release our dataset, model, and demo to foster future research in the area of multimodal instruction following. |
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2023-09-19T00:00:00 | 2309.08628 | Recovering from Privacy-Preserving Masking with Large Language Models | [
"Arpita Vats",
"Zhe Liu",
"Peng Su",
"Debjyoti Paul",
"Yingyi Ma",
"Yutong Pang",
"Zeeshan Ahmed",
"Ozlem Kalinli"
] | Model adaptation is crucial to handle the discrepancy between proxy training data and actual users data received. To effectively perform adaptation, textual data of users is typically stored on servers or their local devices, where downstream natural language processing (NLP) models can be directly trained using such in-domain data. However, this might raise privacy and security concerns due to the extra risks of exposing user information to adversaries. Replacing identifying information in textual data with a generic marker has been recently explored. In this work, we leverage large language models (LLMs) to suggest substitutes of masked tokens and have their effectiveness evaluated on downstream language modeling tasks. Specifically, we propose multiple pre-trained and fine-tuned LLM-based approaches and perform empirical studies on various datasets for the comparison of these methods. Experimental results show that models trained on the obfuscation corpora are able to achieve comparable performance with the ones trained on the original data without privacy-preserving token masking. |
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2023-09-19T00:00:00 | 2309.06497 | A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale | [
"Hao-Jun Michael Shi",
"Tsung-Hsien Lee",
"Shintaro Iwasaki",
"Jose Gallego-Posada",
"Zhijing Li",
"Kaushik Rangadurai",
"Dheevatsa Mudigere",
"Michael Rabbat"
] | Shampoo is an online and stochastic optimization algorithm belonging to the AdaGrad family of methods for training neural networks. It constructs a block-diagonal preconditioner where each block consists of a coarse Kronecker product approximation to full-matrix AdaGrad for each parameter of the neural network. In this work, we provide a complete description of the algorithm as well as the performance optimizations that our implementation leverages to train deep networks at-scale in PyTorch. Our implementation enables fast multi-GPU distributed data-parallel training by distributing the memory and computation associated with blocks of each parameter via PyTorch's DTensor data structure and performing an AllGather primitive on the computed search directions at each iteration. This major performance enhancement enables us to achieve at most a 10% performance reduction in per-step wall-clock time compared against standard diagonal-scaling-based adaptive gradient methods. We validate our implementation by performing an ablation study on training ImageNet ResNet50, demonstrating Shampoo's superiority over standard training recipes with minimal hyperparameter tuning. |
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2023-09-20T00:00:00 | 2309.10020 | Multimodal Foundation Models: From Specialists to General-Purpose Assistants | [
"Chunyuan Li",
"Zhe Gan",
"Zhengyuan Yang",
"Jianwei Yang",
"Linjie Li",
"Lijuan Wang",
"Jianfeng Gao"
] | This paper presents a comprehensive survey of the taxonomy and evolution of multimodal foundation models that demonstrate vision and vision-language capabilities, focusing on the transition from specialist models to general-purpose assistants. The research landscape encompasses five core topics, categorized into two classes. (i) We start with a survey of well-established research areas: multimodal foundation models pre-trained for specific purposes, including two topics -- methods of learning vision backbones for visual understanding and text-to-image generation. (ii) Then, we present recent advances in exploratory, open research areas: multimodal foundation models that aim to play the role of general-purpose assistants, including three topics -- unified vision models inspired by large language models (LLMs), end-to-end training of multimodal LLMs, and chaining multimodal tools with LLMs. The target audiences of the paper are researchers, graduate students, and professionals in computer vision and vision-language multimodal communities who are eager to learn the basics and recent advances in multimodal foundation models. |
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2023-09-20T00:00:00 | 2309.10668 | Language Modeling Is Compression | [
"Grégoire Delétang",
"Anian Ruoss",
"Paul-Ambroise Duquenne",
"Elliot Catt",
"Tim Genewein",
"Christopher Mattern",
"Jordi Grau-Moya",
"Li Kevin Wenliang",
"Matthew Aitchison",
"Laurent Orseau",
"Marcus Hutter",
"Joel Veness"
] | It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community has focused on training increasingly large and powerful self-supervised (language) models. Since these large language models exhibit impressive predictive capabilities, they are well-positioned to be strong compressors. In this work, we advocate for viewing the prediction problem through the lens of compression and evaluate the compression capabilities of large (foundation) models. We show that large language models are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning. For example, Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to 43.4% and LibriSpeech samples to 16.4% of their raw size, beating domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively. Finally, we show that the prediction-compression equivalence allows us to use any compressor (like gzip) to build a conditional generative model. |
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2023-09-20T00:00:00 | 2309.10305 | Baichuan 2: Open Large-scale Language Models | [
"Aiyuan Yang",
"Bin Xiao",
"Bingning Wang",
"Borong Zhang",
"Chao Yin",
"Chenxu Lv",
"Da Pan",
"Dian Wang",
"Dong Yan",
"Fan Yang",
"Fei Deng",
"Feng Wang",
"Feng Liu",
"Guangwei Ai",
"Guosheng Dong Haizhou Zhao",
"Hang Xu",
"Haoze Sun",
"Hongda Zhang",
"Hui Liu",
"Jiaming Ji",
"Jian Xie",
"Juntao Dai",
"Kun Fang",
"Lei Su Liang Song",
"Lifeng Liu",
"Liyun Ru",
"Luyao Ma",
"Mang Wang",
"Mickel Liu",
"MingAn Lin",
"Nuolan Nie",
"Peidong Guo",
"Ruiyang Sun",
"Tao Zhang",
"Tianpeng Li",
"Tianyu Li",
"Wei Cheng",
"Weipeng Chen",
"Xiangrong Zeng",
"Xiaochuan Wang",
"Xiaoxi Chen",
"Xin Men",
"Xin Yu",
"Xuehai Pan",
"Yanjun Shen",
"Yiding Wang",
"Yiyu Li",
"Youxin Jiang",
"Yuchen Gao",
"Yupeng Zhang",
"Zenan Zhou",
"Zhiying Wu"
] | https://github.com/baichuan-inc/Baichuan2 | Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2. |
2023-09-20T00:00:00 | 2309.10706 | OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch | [
"Juntao Li",
"Zecheng Tang",
"Yuyang Ding",
"Pinzheng Wang",
"Pei Guo",
"Wangjie You",
"Dan Qiao",
"Wenliang Chen",
"Guohong Fu",
"Qiaoming Zhu",
"Guodong Zhou",
"Min Zhang"
] | https://github.com/OpenNLG/openBA.git | Large language models (LLMs) with billions of parameters have demonstrated outstanding performance on various natural language processing tasks. This report presents OpenBA, an open-sourced 15B bilingual asymmetric seq2seq model, to contribute an LLM variant to the Chinese-oriented open-source model community. We enhance OpenBA with effective and efficient techniques as well as adopt a three-stage training strategy to train the model from scratch. Our solution can also achieve very competitive performance with only 380B tokens, which is better than LLaMA-70B on the BELEBELE benchmark, BLOOM-176B on the MMLU benchmark, GLM-130B on the C-Eval (hard) benchmark. This report provides the main details to pre-train an analogous model, including pre-training data processing, Bilingual Flan data collection, the empirical observations that inspire our model architecture design, training objectives of different stages, and other enhancement techniques. We have refactored our code to follow the design principles of the Huggingface Transformers Library, making it more convenient for developers to use, and released checkpoints of different training stages at https://huggingface.co/openBA. More details of our project are available at https://github.com/OpenNLG/openBA.git. |
2023-09-20T00:00:00 | 2309.10279 | 360^circ Reconstruction From a Single Image Using Space Carved Outpainting | [
"Nuri Ryu",
"Minsu Gong",
"Geonung Kim",
"Joo-Haeng Lee",
"Sunghyun Cho"
] | We introduce POP3D, a novel framework that creates a full 360^circ-view 3D model from a single image. POP3D resolves two prominent issues that limit the single-view reconstruction. Firstly, POP3D offers substantial generalizability to arbitrary categories, a trait that previous methods struggle to achieve. Secondly, POP3D further improves reconstruction fidelity and naturalness, a crucial aspect that concurrent works fall short of. Our approach marries the strengths of four primary components: (1) a monocular depth and normal predictor that serves to predict crucial geometric cues, (2) a space carving method capable of demarcating the potentially unseen portions of the target object, (3) a generative model pre-trained on a large-scale image dataset that can complete unseen regions of the target, and (4) a neural implicit surface reconstruction method tailored in reconstructing objects using RGB images along with monocular geometric cues. The combination of these components enables POP3D to readily generalize across various in-the-wild images and generate state-of-the-art reconstructions, outperforming similar works by a significant margin. Project page: http://cg.postech.ac.kr/research/POP3D |
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2023-09-20T00:00:00 | 2309.10150 | Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions | [
"Yevgen Chebotar",
"Quan Vuong",
"Alex Irpan",
"Karol Hausman",
"Fei Xia",
"Yao Lu",
"Aviral Kumar",
"Tianhe Yu",
"Alexander Herzog",
"Karl Pertsch",
"Keerthana Gopalakrishnan",
"Julian Ibarz",
"Ofir Nachum",
"Sumedh Sontakke",
"Grecia Salazar",
"Huong T Tran",
"Jodilyn Peralta",
"Clayton Tan",
"Deeksha Manjunath",
"Jaspiar Singht",
"Brianna Zitkovich",
"Tomas Jackson",
"Kanishka Rao",
"Chelsea Finn",
"Sergey Levine"
] | In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data. Our method uses a Transformer to provide a scalable representation for Q-functions trained via offline temporal difference backups. We therefore refer to the method as Q-Transformer. By discretizing each action dimension and representing the Q-value of each action dimension as separate tokens, we can apply effective high-capacity sequence modeling techniques for Q-learning. We present several design decisions that enable good performance with offline RL training, and show that Q-Transformer outperforms prior offline RL algorithms and imitation learning techniques on a large diverse real-world robotic manipulation task suite. The project's website and videos can be found at https://q-transformer.github.io |
|
2023-09-20T00:00:00 | 2309.10537 | FoleyGen: Visually-Guided Audio Generation | [
"Xinhao Mei",
"Varun Nagaraja",
"Gael Le Lan",
"Zhaoheng Ni",
"Ernie Chang",
"Yangyang Shi",
"Vikas Chandra"
] | Recent advancements in audio generation have been spurred by the evolution of large-scale deep learning models and expansive datasets. However, the task of video-to-audio (V2A) generation continues to be a challenge, principally because of the intricate relationship between the high-dimensional visual and auditory data, and the challenges associated with temporal synchronization. In this study, we introduce FoleyGen, an open-domain V2A generation system built on a language modeling paradigm. FoleyGen leverages an off-the-shelf neural audio codec for bidirectional conversion between waveforms and discrete tokens. The generation of audio tokens is facilitated by a single Transformer model, which is conditioned on visual features extracted from a visual encoder. A prevalent problem in V2A generation is the misalignment of generated audio with the visible actions in the video. To address this, we explore three novel visual attention mechanisms. We further undertake an exhaustive evaluation of multiple visual encoders, each pretrained on either single-modal or multi-modal tasks. The experimental results on VGGSound dataset show that our proposed FoleyGen outperforms previous systems across all objective metrics and human evaluations. |
|
2023-09-20T00:00:00 | 2309.10818 | SlimPajama-DC: Understanding Data Combinations for LLM Training | [
"Zhiqiang Shen",
"Tianhua Tao",
"Liqun Ma",
"Willie Neiswanger",
"Joel Hestness",
"Natalia Vassilieva",
"Daria Soboleva",
"Eric Xing"
] | This paper aims to understand the impacts of various data combinations (e.g., web text, wikipedia, github, books) on the training of large language models using SlimPajama. SlimPajama is a rigorously deduplicated, multi-source dataset, which has been refined and further deduplicated to 627B tokens from the extensive 1.2T tokens RedPajama dataset contributed by Together. We've termed our research as SlimPajama-DC, an empirical analysis designed to uncover fundamental characteristics and best practices associated with employing SlimPajama in the training of large language models. During our research with SlimPajama, two pivotal observations emerged: (1) Global deduplication vs. local deduplication. We analyze and discuss how global (across different sources of datasets) and local (within the single source of dataset) deduplications affect the performance of trained models. (2) Proportions of high-quality/highly-deduplicated multi-source datasets in the combination. To study this, we construct six configurations of SlimPajama dataset and train individual ones using 1.3B Cerebras-GPT model with Alibi and SwiGLU. Our best configuration outperforms the 1.3B model trained on RedPajama using the same number of training tokens by a significant margin. All our 1.3B models are trained on Cerebras 16times CS-2 cluster with a total of 80 PFLOP/s in bf16 mixed precision. We further extend our discoveries (such as increasing data diversity is crucial after global deduplication) on a 7B model with large batch-size training. Our models and the separate SlimPajama-DC datasets are available at: https://huggingface.co/MBZUAI-LLM and https://huggingface.co/datasets/cerebras/SlimPajama-627B. |
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2023-09-20T00:00:00 | 2309.10202 | Stabilizing RLHF through Advantage Model and Selective Rehearsal | [
"Baolin Peng",
"Linfeng Song",
"Ye Tian",
"Lifeng Jin",
"Haitao Mi",
"Dong Yu"
] | Large Language Models (LLMs) have revolutionized natural language processing, yet aligning these models with human values and preferences using RLHF remains a significant challenge. This challenge is characterized by various instabilities, such as reward hacking and catastrophic forgetting. In this technical report, we propose two innovations to stabilize RLHF training: 1) Advantage Model, which directly models advantage score i.e., extra reward compared to the expected rewards and regulates score distributions across tasks to prevent reward hacking. 2) Selective Rehearsal, which mitigates catastrophic forgetting by strategically selecting data for PPO training and knowledge rehearsing. Our experimental analysis on public and proprietary datasets reveals that the proposed methods not only increase stability in RLHF training but also achieve higher reward scores and win rates. |
|
2023-09-21T00:00:00 | 2309.10952 | LMDX: Language Model-based Document Information Extraction and Localization | [
"Vincent Perot",
"Kai Kang",
"Florian Luisier",
"Guolong Su",
"Xiaoyu Sun",
"Ramya Sree Boppana",
"Zilong Wang",
"Jiaqi Mu",
"Hao Zhang",
"Nan Hua"
] | Large Language Models (LLM) have revolutionized Natural Language Processing (NLP), improving state-of-the-art on many existing tasks and exhibiting emergent capabilities. However, LLMs have not yet been successfully applied on semi-structured document information extraction, which is at the core of many document processing workflows and consists of extracting key entities from a visually rich document (VRD) given a predefined target schema. The main obstacles to LLM adoption in that task have been the absence of layout encoding within LLMs, critical for a high quality extraction, and the lack of a grounding mechanism ensuring the answer is not hallucinated. In this paper, we introduce Language Model-based Document Information Extraction and Localization (LMDX), a methodology to adapt arbitrary LLMs for document information extraction. LMDX can do extraction of singular, repeated, and hierarchical entities, both with and without training data, while providing grounding guarantees and localizing the entities within the document. In particular, we apply LMDX to the PaLM 2-S LLM and evaluate it on VRDU and CORD benchmarks, setting a new state-of-the-art and showing how LMDX enables the creation of high quality, data-efficient parsers. |
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2023-09-21T00:00:00 | 2309.11499 | DreamLLM: Synergistic Multimodal Comprehension and Creation | [
"Runpei Dong",
"Chunrui Han",
"Yuang Peng",
"Zekun Qi",
"Zheng Ge",
"Jinrong Yang",
"Liang Zhao",
"Jianjian Sun",
"Hongyu Zhou",
"Haoran Wei",
"Xiangwen Kong",
"Xiangyu Zhang",
"Kaisheng Ma",
"Li Yi"
] | https://github.com/RunpeiDong/DreamLLM | This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation. DreamLLM operates on two fundamental principles. The first focuses on the generative modeling of both language and image posteriors by direct sampling in the raw multimodal space. This approach circumvents the limitations and information loss inherent to external feature extractors like CLIP, and a more thorough multimodal understanding is obtained. Second, DreamLLM fosters the generation of raw, interleaved documents, modeling both text and image contents, along with unstructured layouts. This allows DreamLLM to learn all conditional, marginal, and joint multimodal distributions effectively. As a result, DreamLLM is the first MLLM capable of generating free-form interleaved content. Comprehensive experiments highlight DreamLLM's superior performance as a zero-shot multimodal generalist, reaping from the enhanced learning synergy. |
2023-09-21T00:00:00 | 2309.11497 | FreeU: Free Lunch in Diffusion U-Net | [
"Chenyang Si",
"Ziqi Huang",
"Yuming Jiang",
"Ziwei Liu"
] | https://github.com/ChenyangSi/FreeU | In this paper, we uncover the untapped potential of diffusion U-Net, which serves as a "free lunch" that substantially improves the generation quality on the fly. We initially investigate the key contributions of the U-Net architecture to the denoising process and identify that its main backbone primarily contributes to denoising, whereas its skip connections mainly introduce high-frequency features into the decoder module, causing the network to overlook the backbone semantics. Capitalizing on this discovery, we propose a simple yet effective method-termed "FreeU" - that enhances generation quality without additional training or finetuning. Our key insight is to strategically re-weight the contributions sourced from the U-Net's skip connections and backbone feature maps, to leverage the strengths of both components of the U-Net architecture. Promising results on image and video generation tasks demonstrate that our FreeU can be readily integrated to existing diffusion models, e.g., Stable Diffusion, DreamBooth, ModelScope, Rerender and ReVersion, to improve the generation quality with only a few lines of code. All you need is to adjust two scaling factors during inference. Project page: https://chenyangsi.top/FreeU/. |
2023-09-21T00:00:00 | 2309.11419 | Kosmos-2.5: A Multimodal Literate Model | [
"Tengchao Lv",
"Yupan Huang",
"Jingye Chen",
"Lei Cui",
"Shuming Ma",
"Yaoyao Chang",
"Shaohan Huang",
"Wenhui Wang",
"Li Dong",
"Weiyao Luo",
"Shaoxiang Wu",
"Guoxin Wang",
"Cha Zhang",
"Furu Wei"
] | We present Kosmos-2.5, a multimodal literate model for machine reading of text-intensive images. Pre-trained on large-scale text-intensive images, Kosmos-2.5 excels in two distinct yet cooperative transcription tasks: (1) generating spatially-aware text blocks, where each block of text is assigned its spatial coordinates within the image, and (2) producing structured text output that captures styles and structures into the markdown format. This unified multimodal literate capability is achieved through a shared Transformer architecture, task-specific prompts, and flexible text representations. We evaluate Kosmos-2.5 on end-to-end document-level text recognition and image-to-markdown text generation. Furthermore, the model can be readily adapted for any text-intensive image understanding task with different prompts through supervised fine-tuning, making it a general-purpose tool for real-world applications involving text-rich images. This work also paves the way for the future scaling of multimodal large language models. |
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2023-09-21T00:00:00 | 2309.11495 | Chain-of-Verification Reduces Hallucination in Large Language Models | [
"Shehzaad Dhuliawala",
"Mojtaba Komeili",
"Jing Xu",
"Roberta Raileanu",
"Xian Li",
"Asli Celikyilmaz",
"Jason Weston"
] | Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models. We study the ability of language models to deliberate on the responses they give in order to correct their mistakes. We develop the Chain-of-Verification (CoVe) method whereby the model first (i) drafts an initial response; then (ii) plans verification questions to fact-check its draft; (iii) answers those questions independently so the answers are not biased by other responses; and (iv) generates its final verified response. In experiments, we show CoVe decreases hallucinations across a variety of tasks, from list-based questions from Wikidata, closed book MultiSpanQA and longform text generation. |
|
2023-09-21T00:00:00 | 2309.10917 | End-to-End Speech Recognition Contextualization with Large Language Models | [
"Egor Lakomkin",
"Chunyang Wu",
"Yassir Fathullah",
"Ozlem Kalinli",
"Michael L. Seltzer",
"Christian Fuegen"
] | In recent years, Large Language Models (LLMs) have garnered significant attention from the research community due to their exceptional performance and generalization capabilities. In this paper, we introduce a novel method for contextualizing speech recognition models incorporating LLMs. Our approach casts speech recognition as a mixed-modal language modeling task based on a pretrained LLM. We provide audio features, along with optional text tokens for context, to train the system to complete transcriptions in a decoder-only fashion. As a result, the system is implicitly incentivized to learn how to leverage unstructured contextual information during training. Our empirical results demonstrate a significant improvement in performance, with a 6% WER reduction when additional textual context is provided. Moreover, we find that our method performs competitively and improve by 7.5% WER overall and 17% WER on rare words against a baseline contextualized RNN-T system that has been trained on more than twenty five times larger speech dataset. Overall, we demonstrate that by only adding a handful number of trainable parameters via adapters, we can unlock contextualized speech recognition capability for the pretrained LLM while keeping the same text-only input functionality. |
|
2023-09-21T00:00:00 | 2309.11197 | The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute | [
"Aleksandar Stanić",
"Dylan Ashley",
"Oleg Serikov",
"Louis Kirsch",
"Francesco Faccio",
"Jürgen Schmidhuber",
"Thomas Hofmann",
"Imanol Schlag"
] | The Languini Kitchen serves as both a research collective and codebase designed to empower researchers with limited computational resources to contribute meaningfully to the field of language modelling. We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours. The number of tokens on which a model is trained is defined by the model's throughput and the chosen compute class. Notably, this approach avoids constraints on critical hyperparameters which affect total parameters or floating-point operations. For evaluation, we pre-process an existing large, diverse, and high-quality dataset of books that surpasses existing academic benchmarks in quality, diversity, and document length. On it, we compare methods based on their empirical scaling trends which are estimated through experiments at various levels of compute. This work also provides two baseline models: a feed-forward model derived from the GPT-2 architecture and a recurrent model in the form of a novel LSTM with ten-fold throughput. While the GPT baseline achieves better perplexity throughout all our levels of compute, our LSTM baseline exhibits a predictable and more favourable scaling law. This is due to the improved throughput and the need for fewer training tokens to achieve the same decrease in test perplexity. Extrapolating the scaling laws leads of both models results in an intersection at roughly 50,000 accelerator hours. We hope this work can serve as the foundation for meaningful and reproducible language modelling research. |
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2023-09-21T00:00:00 | 2309.11500 | A Large-scale Dataset for Audio-Language Representation Learning | [
"Luoyi Sun",
"Xuenan Xu",
"Mengyue Wu",
"Weidi Xie"
] | The AI community has made significant strides in developing powerful foundation models, driven by large-scale multimodal datasets. However, in the audio representation learning community, the present audio-language datasets suffer from limitations such as insufficient volume, simplistic content, and arduous collection procedures. To tackle these challenges, we present an innovative and automatic audio caption generation pipeline based on a series of public tools or APIs, and construct a large-scale, high-quality, audio-language dataset, named as Auto-ACD, comprising over 1.9M audio-text pairs. To demonstrate the effectiveness of the proposed dataset, we train popular models on our dataset and show performance improvement on various downstream tasks, namely, audio-language retrieval, audio captioning, environment classification. In addition, we establish a novel test set and provide a benchmark for audio-text tasks. The proposed dataset will be released at https://auto-acd.github.io/. |
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2023-09-21T00:00:00 | 2309.11009 | Controllable Dynamic Appearance for Neural 3D Portraits | [
"ShahRukh Athar",
"Zhixin Shu",
"Zexiang Xu",
"Fuji Luan",
"Sai Bi",
"Kalyan Sunkavalli",
"Dimitris Samaras"
] | Recent advances in Neural Radiance Fields (NeRFs) have made it possible to reconstruct and reanimate dynamic portrait scenes with control over head-pose, facial expressions and viewing direction. However, training such models assumes photometric consistency over the deformed region e.g. the face must be evenly lit as it deforms with changing head-pose and facial expression. Such photometric consistency across frames of a video is hard to maintain, even in studio environments, thus making the created reanimatable neural portraits prone to artifacts during reanimation. In this work, we propose CoDyNeRF, a system that enables the creation of fully controllable 3D portraits in real-world capture conditions. CoDyNeRF learns to approximate illumination dependent effects via a dynamic appearance model in the canonical space that is conditioned on predicted surface normals and the facial expressions and head-pose deformations. The surface normals prediction is guided using 3DMM normals that act as a coarse prior for the normals of the human head, where direct prediction of normals is hard due to rigid and non-rigid deformations induced by head-pose and facial expression changes. Using only a smartphone-captured short video of a subject for training, we demonstrate the effectiveness of our method on free view synthesis of a portrait scene with explicit head pose and expression controls, and realistic lighting effects. The project page can be found here: http://shahrukhathar.github.io/2023/08/22/CoDyNeRF.html |
|
2023-09-22T00:00:00 | 2309.11674 | A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models | [
"Haoran Xu",
"Young Jin Kim",
"Amr Sharaf",
"Hany Hassan Awadalla"
] | Generative Large Language Models (LLMs) have achieved remarkable advancements in various NLP tasks. However, these advances have not been reflected in the translation task, especially those with moderate model sizes (i.e., 7B or 13B parameters), which still lag behind conventional supervised encoder-decoder translation models. Previous studies have attempted to improve the translation capabilities of these moderate LLMs, but their gains have been limited. In this study, we propose a novel fine-tuning approach for LLMs that is specifically designed for the translation task, eliminating the need for the abundant parallel data that traditional translation models usually depend on. Our approach consists of two fine-tuning stages: initial fine-tuning on monolingual data followed by subsequent fine-tuning on a small set of high-quality parallel data. We introduce the LLM developed through this strategy as Advanced Language Model-based trAnslator (ALMA). Based on LLaMA-2 as our underlying model, our results show that the model can achieve an average improvement of more than 12 BLEU and 12 COMET over its zero-shot performance across 10 translation directions from the WMT'21 (2 directions) and WMT'22 (8 directions) test datasets. The performance is significantly better than all prior work and even superior to the NLLB-54B model and GPT-3.5-text-davinci-003, with only 7B or 13B parameters. This method establishes the foundation for a novel training paradigm in machine translation. |
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2023-09-22T00:00:00 | 2309.11568 | BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model | [
"Nolan Dey",
"Daria Soboleva",
"Faisal Al-Khateeb",
"Bowen Yang",
"Ribhu Pathria",
"Hemant Khachane",
"Shaheer Muhammad",
"Zhiming",
"Chen",
"Robert Myers",
"Jacob Robert Steeves",
"Natalia Vassilieva",
"Marvin Tom",
"Joel Hestness"
] | We introduce the Bittensor Language Model, called "BTLM-3B-8K", a new state-of-the-art 3 billion parameter open-source language model. BTLM-3B-8K was trained on 627B tokens from the SlimPajama dataset with a mixture of 2,048 and 8,192 context lengths. BTLM-3B-8K outperforms all existing 3B parameter models by 2-5.5% across downstream tasks. BTLM-3B-8K is even competitive with some 7B parameter models. Additionally, BTLM-3B-8K provides excellent long context performance, outperforming MPT-7B-8K and XGen-7B-8K on tasks up to 8,192 context length. We trained the model on a cleaned and deduplicated SlimPajama dataset; aggressively tuned the \textmu P hyperparameters and schedule; used ALiBi position embeddings; and adopted the SwiGLU nonlinearity. On Hugging Face, the most popular models have 7B parameters, indicating that users prefer the quality-size ratio of 7B models. Compacting the 7B parameter model to one with 3B parameters, with little performance impact, is an important milestone. BTLM-3B-8K needs only 3GB of memory with 4-bit precision and takes 2.5x less inference compute than 7B models, helping to open up access to a powerful language model on mobile and edge devices. BTLM-3B-8K is available under an Apache 2.0 license on Hugging Face: https://huggingface.co/cerebras/btlm-3b-8k-base. |
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2023-09-22T00:00:00 | 2309.11998 | LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset | [
"Lianmin Zheng",
"Wei-Lin Chiang",
"Ying Sheng",
"Tianle Li",
"Siyuan Zhuang",
"Zhanghao Wu",
"Yonghao Zhuang",
"Zhuohan Li",
"Zi Lin",
"Eric. P Xing",
"Joseph E. Gonzalez",
"Ion Stoica",
"Hao Zhang"
] | Studying how people interact with large language models (LLMs) in real-world scenarios is increasingly important due to their widespread use in various applications. In this paper, we introduce LMSYS-Chat-1M, a large-scale dataset containing one million real-world conversations with 25 state-of-the-art LLMs. This dataset is collected from 210K unique IP addresses in the wild on our Vicuna demo and Chatbot Arena website. We offer an overview of the dataset's content, including its curation process, basic statistics, and topic distribution, highlighting its diversity, originality, and scale. We demonstrate its versatility through four use cases: developing content moderation models that perform similarly to GPT-4, building a safety benchmark, training instruction-following models that perform similarly to Vicuna, and creating challenging benchmark questions. We believe that this dataset will serve as a valuable resource for understanding and advancing LLM capabilities. The dataset is publicly available at https://huggingface.co/datasets/lmsys/lmsys-chat-1m. |
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2023-09-22T00:00:00 | 2309.12307 | LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models | [
"Yukang Chen",
"Shengju Qian",
"Haotian Tang",
"Xin Lai",
"Zhijian Liu",
"Song Han",
"Jiaya Jia"
] | https://github.com/dvlab-research/LongLoRA | We present LongLoRA, an efficient fine-tuning approach that extends the context sizes of pre-trained large language models (LLMs), with limited computation cost. Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources. For example, training on the context length of 8192 needs 16x computational costs in self-attention layers as that of 2048. In this paper, we speed up the context extension of LLMs in two aspects. On the one hand, although dense global attention is needed during inference, fine-tuning the model can be effectively and efficiently done by sparse local attention. The proposed shift short attention effectively enables context extension, leading to non-trivial computation saving with similar performance to fine-tuning with vanilla attention. Particularly, it can be implemented with only two lines of code in training, while being optional in inference. On the other hand, we revisit the parameter-efficient fine-tuning regime for context expansion. Notably, we find that LoRA for context extension works well under the premise of trainable embedding and normalization. LongLoRA demonstrates strong empirical results on various tasks on LLaMA2 models from 7B/13B to 70B. LongLoRA adopts LLaMA2 7B from 4k context to 100k, or LLaMA2 70B to 32k on a single 8x A100 machine. LongLoRA extends models' context while retaining their original architectures, and is compatible with most existing techniques, like FlashAttention-2. In addition, to make LongLoRA practical, we collect a dataset, LongQA, for supervised fine-tuning. It contains more than 3k long context question-answer pairs. |
2023-09-22T00:00:00 | 2309.11523 | RMT: Retentive Networks Meet Vision Transformers | [
"Qihang Fan",
"Huaibo Huang",
"Mingrui Chen",
"Hongmin Liu",
"Ran He"
] | Transformer first appears in the field of natural language processing and is later migrated to the computer vision domain, where it demonstrates excellent performance in vision tasks. However, recently, Retentive Network (RetNet) has emerged as an architecture with the potential to replace Transformer, attracting widespread attention in the NLP community. Therefore, we raise the question of whether transferring RetNet's idea to vision can also bring outstanding performance to vision tasks. To address this, we combine RetNet and Transformer to propose RMT. Inspired by RetNet, RMT introduces explicit decay into the vision backbone, bringing prior knowledge related to spatial distances to the vision model. This distance-related spatial prior allows for explicit control of the range of tokens that each token can attend to. Additionally, to reduce the computational cost of global modeling, we decompose this modeling process along the two coordinate axes of the image. Abundant experiments have demonstrated that our RMT exhibits exceptional performance across various computer vision tasks. For example, RMT achieves 84.1% Top1-acc on ImageNet-1k using merely 4.5G FLOPs. To the best of our knowledge, among all models, RMT achieves the highest Top1-acc when models are of similar size and trained with the same strategy. Moreover, RMT significantly outperforms existing vision backbones in downstream tasks such as object detection, instance segmentation, and semantic segmentation. Our work is still in progress. |
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2023-09-22T00:00:00 | 2309.12311 | LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent | [
"Jianing Yang",
"Xuweiyi Chen",
"Shengyi Qian",
"Nikhil Madaan",
"Madhavan Iyengar",
"David F. Fouhey",
"Joyce Chai"
] | 3D visual grounding is a critical skill for household robots, enabling them to navigate, manipulate objects, and answer questions based on their environment. While existing approaches often rely on extensive labeled data or exhibit limitations in handling complex language queries, we propose LLM-Grounder, a novel zero-shot, open-vocabulary, Large Language Model (LLM)-based 3D visual grounding pipeline. LLM-Grounder utilizes an LLM to decompose complex natural language queries into semantic constituents and employs a visual grounding tool, such as OpenScene or LERF, to identify objects in a 3D scene. The LLM then evaluates the spatial and commonsense relations among the proposed objects to make a final grounding decision. Our method does not require any labeled training data and can generalize to novel 3D scenes and arbitrary text queries. We evaluate LLM-Grounder on the ScanRefer benchmark and demonstrate state-of-the-art zero-shot grounding accuracy. Our findings indicate that LLMs significantly improve the grounding capability, especially for complex language queries, making LLM-Grounder an effective approach for 3D vision-language tasks in robotics. Videos and interactive demos can be found on the project website https://chat-with-nerf.github.io/ . |
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2023-09-22T00:00:00 | 2309.12284 | MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models | [
"Longhui Yu",
"Weisen Jiang",
"Han Shi",
"Jincheng Yu",
"Zhengying Liu",
"Yu Zhang",
"James T. Kwok",
"Zhenguo Li",
"Adrian Weller",
"Weiyang Liu"
] | Large language models (LLMs) have pushed the limits of natural language understanding and exhibited excellent problem-solving ability. Despite the great success, most existing open-source LLMs (\eg, LLaMA-2) are still far away from satisfactory for solving mathematical problem due to the complex reasoning procedures. To bridge this gap, we propose MetaMath, a fine-tuned language model that specializes in mathematical reasoning. Specifically, we start by bootstrapping mathematical questions by rewriting the question from multiple perspectives without extra knowledge, which results in a new dataset called {MetaMathQA}. Then we fine-tune the LLaMA-2 models on MetaMathQA. Experimental results on two popular benchmarks (\ie, GSM8K and MATH) for mathematical reasoning demonstrate that MetaMath outperforms a suite of open-source LLMs by a significant margin. Our MetaMath-7B model achieves 66.4% on GSM8K and 19.4% on MATH, exceeding the state-of-the-art models of the same size by 11.5% and 8.7%. Particularly, {MetaMath-70B} achieves an accuracy of 82.3% on {GSM8K}, slightly better than {GPT-3.5-Turbo}. We release the {MetaMathQA} dataset, the {MetaMath} models with different model sizes and the training code for public use. |
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2023-09-22T00:00:00 | 2309.12207 | Boolformer: Symbolic Regression of Logic Functions with Transformers | [
"Stéphane d'Ascoli",
"Samy Bengio",
"Josh Susskind",
"Emmanuel Abbé"
] | In this work, we introduce Boolformer, the first Transformer architecture trained to perform end-to-end symbolic regression of Boolean functions. First, we show that it can predict compact formulas for complex functions which were not seen during training, when provided a clean truth table. Then, we demonstrate its ability to find approximate expressions when provided incomplete and noisy observations. We evaluate the Boolformer on a broad set of real-world binary classification datasets, demonstrating its potential as an interpretable alternative to classic machine learning methods. Finally, we apply it to the widespread task of modelling the dynamics of gene regulatory networks. Using a recent benchmark, we show that Boolformer is competitive with state-of-the art genetic algorithms with a speedup of several orders of magnitude. Our code and models are available publicly. |
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2023-09-25T00:00:00 | 2309.12499 | CodePlan: Repository-level Coding using LLMs and Planning | [
"Ramakrishna Bairi",
"Atharv Sonwane",
"Aditya Kanade",
"Vageesh D C",
"Arun Iyer",
"Suresh Parthasarathy",
"Sriram Rajamani",
"B. Ashok",
"Shashank Shet"
] | Software engineering activities such as package migration, fixing errors reports from static analysis or testing, and adding type annotations or other specifications to a codebase, involve pervasively editing the entire repository of code. We formulate these activities as repository-level coding tasks. Recent tools like GitHub Copilot, which are powered by Large Language Models (LLMs), have succeeded in offering high-quality solutions to localized coding problems. Repository-level coding tasks are more involved and cannot be solved directly using LLMs, since code within a repository is inter-dependent and the entire repository may be too large to fit into the prompt. We frame repository-level coding as a planning problem and present a task-agnostic framework, called CodePlan to solve it. CodePlan synthesizes a multi-step chain of edits (plan), where each step results in a call to an LLM on a code location with context derived from the entire repository, previous code changes and task-specific instructions. CodePlan is based on a novel combination of an incremental dependency analysis, a change may-impact analysis and an adaptive planning algorithm. We evaluate the effectiveness of CodePlan on two repository-level tasks: package migration (C#) and temporal code edits (Python). Each task is evaluated on multiple code repositories, each of which requires inter-dependent changes to many files (between 2-97 files). Coding tasks of this level of complexity have not been automated using LLMs before. Our results show that CodePlan has better match with the ground truth compared to baselines. CodePlan is able to get 5/6 repositories to pass the validity checks (e.g., to build without errors and make correct code edits) whereas the baselines (without planning but with the same type of contextual information as CodePlan) cannot get any of the repositories to pass them. |
|
2023-09-25T00:00:00 | 2309.13042 | MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary Instance Segmentation | [
"Jiahao Xie",
"Wei Li",
"Xiangtai Li",
"Ziwei Liu",
"Yew Soon Ong",
"Chen Change Loy"
] | https://github.com/Jiahao000/MosaicFusion | We present MosaicFusion, a simple yet effective diffusion-based data augmentation approach for large vocabulary instance segmentation. Our method is training-free and does not rely on any label supervision. Two key designs enable us to employ an off-the-shelf text-to-image diffusion model as a useful dataset generator for object instances and mask annotations. First, we divide an image canvas into several regions and perform a single round of diffusion process to generate multiple instances simultaneously, conditioning on different text prompts. Second, we obtain corresponding instance masks by aggregating cross-attention maps associated with object prompts across layers and diffusion time steps, followed by simple thresholding and edge-aware refinement processing. Without bells and whistles, our MosaicFusion can produce a significant amount of synthetic labeled data for both rare and novel categories. Experimental results on the challenging LVIS long-tailed and open-vocabulary benchmarks demonstrate that MosaicFusion can significantly improve the performance of existing instance segmentation models, especially for rare and novel categories. Code will be released at https://github.com/Jiahao000/MosaicFusion. |
2023-09-25T00:00:00 | 2309.13018 | Dynamic ASR Pathways: An Adaptive Masking Approach Towards Efficient Pruning of A Multilingual ASR Model | [
"Jiamin Xie",
"Ke Li",
"Jinxi Guo",
"Andros Tjandra",
"Yuan Shangguan",
"Leda Sari",
"Chunyang Wu",
"Junteng Jia",
"Jay Mahadeokar",
"Ozlem Kalinli"
] | Neural network pruning offers an effective method for compressing a multilingual automatic speech recognition (ASR) model with minimal performance loss. However, it entails several rounds of pruning and re-training needed to be run for each language. In this work, we propose the use of an adaptive masking approach in two scenarios for pruning a multilingual ASR model efficiently, each resulting in sparse monolingual models or a sparse multilingual model (named as Dynamic ASR Pathways). Our approach dynamically adapts the sub-network, avoiding premature decisions about a fixed sub-network structure. We show that our approach outperforms existing pruning methods when targeting sparse monolingual models. Further, we illustrate that Dynamic ASR Pathways jointly discovers and trains better sub-networks (pathways) of a single multilingual model by adapting from different sub-network initializations, thereby reducing the need for language-specific pruning. |
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2023-09-25T00:00:00 | 2309.13041 | Robotic Offline RL from Internet Videos via Value-Function Pre-Training | [
"Chethan Bhateja",
"Derek Guo",
"Dibya Ghosh",
"Anikait Singh",
"Manan Tomar",
"Quan Vuong",
"Yevgen Chebotar",
"Sergey Levine",
"Aviral Kumar"
] | Pre-training on Internet data has proven to be a key ingredient for broad generalization in many modern ML systems. What would it take to enable such capabilities in robotic reinforcement learning (RL)? Offline RL methods, which learn from datasets of robot experience, offer one way to leverage prior data into the robotic learning pipeline. However, these methods have a "type mismatch" with video data (such as Ego4D), the largest prior datasets available for robotics, since video offers observation-only experience without the action or reward annotations needed for RL methods. In this paper, we develop a system for leveraging large-scale human video datasets in robotic offline RL, based entirely on learning value functions via temporal-difference learning. We show that value learning on video datasets learns representations that are more conducive to downstream robotic offline RL than other approaches for learning from video data. Our system, called V-PTR, combines the benefits of pre-training on video data with robotic offline RL approaches that train on diverse robot data, resulting in value functions and policies for manipulation tasks that perform better, act robustly, and generalize broadly. On several manipulation tasks on a real WidowX robot, our framework produces policies that greatly improve over prior methods. Our video and additional details can be found at https://dibyaghosh.com/vptr/ |
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2023-09-25T00:00:00 | 2309.12424 | DualToken-ViT: Position-aware Efficient Vision Transformer with Dual Token Fusion | [
"Zhenzhen Chu",
"Jiayu Chen",
"Cen Chen",
"Chengyu Wang",
"Ziheng Wu",
"Jun Huang",
"Weining Qian"
] | Self-attention-based vision transformers (ViTs) have emerged as a highly competitive architecture in computer vision. Unlike convolutional neural networks (CNNs), ViTs are capable of global information sharing. With the development of various structures of ViTs, ViTs are increasingly advantageous for many vision tasks. However, the quadratic complexity of self-attention renders ViTs computationally intensive, and their lack of inductive biases of locality and translation equivariance demands larger model sizes compared to CNNs to effectively learn visual features. In this paper, we propose a light-weight and efficient vision transformer model called DualToken-ViT that leverages the advantages of CNNs and ViTs. DualToken-ViT effectively fuses the token with local information obtained by convolution-based structure and the token with global information obtained by self-attention-based structure to achieve an efficient attention structure. In addition, we use position-aware global tokens throughout all stages to enrich the global information, which further strengthening the effect of DualToken-ViT. Position-aware global tokens also contain the position information of the image, which makes our model better for vision tasks. We conducted extensive experiments on image classification, object detection and semantic segmentation tasks to demonstrate the effectiveness of DualToken-ViT. On the ImageNet-1K dataset, our models of different scales achieve accuracies of 75.4% and 79.4% with only 0.5G and 1.0G FLOPs, respectively, and our model with 1.0G FLOPs outperforms LightViT-T using global tokens by 0.7%. |
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2023-09-26T00:00:00 | 2309.13075 | SCREWS: A Modular Framework for Reasoning with Revisions | [
"Kumar Shridhar",
"Harsh Jhamtani",
"Hao Fang",
"Benjamin Van Durme",
"Jason Eisner",
"Patrick Xia"
] | Large language models (LLMs) can improve their accuracy on various tasks through iteratively refining and revising their output based on feedback. We observe that these revisions can introduce errors, in which case it is better to roll back to a previous result. Further, revisions are typically homogeneous: they use the same reasoning method that produced the initial answer, which may not correct errors. To enable exploration in this space, we present SCREWS, a modular framework for reasoning with revisions. It is comprised of three main modules: Sampling, Conditional Resampling, and Selection, each consisting of sub-modules that can be hand-selected per task. We show that SCREWS not only unifies several previous approaches under a common framework, but also reveals several novel strategies for identifying improved reasoning chains. We evaluate our framework with state-of-the-art LLMs (ChatGPT and GPT-4) on a diverse set of reasoning tasks and uncover useful new reasoning strategies for each: arithmetic word problems, multi-hop question answering, and code debugging. Heterogeneous revision strategies prove to be important, as does selection between original and revised candidates. |
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2023-09-26T00:00:00 | 2309.13952 | VidChapters-7M: Video Chapters at Scale | [
"Antoine Yang",
"Arsha Nagrani",
"Ivan Laptev",
"Josef Sivic",
"Cordelia Schmid"
] | Segmenting long videos into chapters enables users to quickly navigate to the information of their interest. This important topic has been understudied due to the lack of publicly released datasets. To address this issue, we present VidChapters-7M, a dataset of 817K user-chaptered videos including 7M chapters in total. VidChapters-7M is automatically created from videos online in a scalable manner by scraping user-annotated chapters and hence without any additional manual annotation. We introduce the following three tasks based on this data. First, the video chapter generation task consists of temporally segmenting the video and generating a chapter title for each segment. To further dissect the problem, we also define two variants of this task: video chapter generation given ground-truth boundaries, which requires generating a chapter title given an annotated video segment, and video chapter grounding, which requires temporally localizing a chapter given its annotated title. We benchmark both simple baselines and state-of-the-art video-language models for these three tasks. We also show that pretraining on VidChapters-7M transfers well to dense video captioning tasks in both zero-shot and finetuning settings, largely improving the state of the art on the YouCook2 and ViTT benchmarks. Finally, our experiments reveal that downstream performance scales well with the size of the pretraining dataset. Our dataset, code, and models are publicly available at https://antoyang.github.io/vidchapters.html. |
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2023-09-26T00:00:00 | 2309.14322 | Small-scale proxies for large-scale Transformer training instabilities | [
"Mitchell Wortsman",
"Peter J. Liu",
"Lechao Xiao",
"Katie Everett",
"Alex Alemi",
"Ben Adlam",
"John D. Co-Reyes",
"Izzeddin Gur",
"Abhishek Kumar",
"Roman Novak",
"Jeffrey Pennington",
"Jascha Sohl-dickstein",
"Kelvin Xu",
"Jaehoon Lee",
"Justin Gilmer",
"Simon Kornblith"
] | Teams that have trained large Transformer-based models have reported training instabilities at large scale that did not appear when training with the same hyperparameters at smaller scales. Although the causes of such instabilities are of scientific interest, the amount of resources required to reproduce them has made investigation difficult. In this work, we seek ways to reproduce and study training stability and instability at smaller scales. First, we focus on two sources of training instability described in previous work: the growth of logits in attention layers (Dehghani et al., 2023) and divergence of the output logits from the log probabilities (Chowdhery et al., 2022). By measuring the relationship between learning rate and loss across scales, we show that these instabilities also appear in small models when training at high learning rates, and that mitigations previously employed at large scales are equally effective in this regime. This prompts us to investigate the extent to which other known optimizer and model interventions influence the sensitivity of the final loss to changes in the learning rate. To this end, we study methods such as warm-up, weight decay, and the muParam (Yang et al., 2022), and combine techniques to train small models that achieve similar losses across orders of magnitude of learning rate variation. Finally, to conclude our exploration we study two cases where instabilities can be predicted before they emerge by examining the scaling behavior of model activation and gradient norms. |
|
2023-09-26T00:00:00 | 2309.14327 | DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention | [
"Zhewei Yao",
"Xiaoxia Wu",
"Conglong Li",
"Minjia Zhang",
"Heyang Qi",
"Olatunji Ruwase",
"Ammar Ahmad Awan",
"Samyam Rajbhandari",
"Yuxiong He"
] | Most of the existing multi-modal models, hindered by their incapacity to adeptly manage interleaved image-and-text inputs in multi-image, multi-round dialogues, face substantial constraints in resource allocation for training and data accessibility, impacting their adaptability and scalability across varied interaction realms. To address this, we present the DeepSpeed-VisualChat framework, designed to optimize Large Language Models (LLMs) by incorporating multi-modal capabilities, with a focus on enhancing the proficiency of Large Vision and Language Models in handling interleaved inputs. Our framework is notable for (1) its open-source support for multi-round and multi-image dialogues, (2) introducing an innovative multi-modal causal attention mechanism, and (3) utilizing data blending techniques on existing datasets to assure seamless interactions in multi-round, multi-image conversations. Compared to existing frameworks, DeepSpeed-VisualChat shows superior scalability up to 70B parameter language model size, representing a significant advancement in multi-modal language models and setting a solid foundation for future explorations. |
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2023-09-26T00:00:00 | 2309.13356 | Exploring Large Language Models' Cognitive Moral Development through Defining Issues Test | [
"Kumar Tanmay",
"Aditi Khandelwal",
"Utkarsh Agarwal",
"Monojit Choudhury"
] | The development of large language models has instilled widespread interest among the researchers to understand their inherent reasoning and problem-solving capabilities. Despite good amount of research going on to elucidate these capabilities, there is a still an appreciable gap in understanding moral development and judgments of these models. The current approaches of evaluating the ethical reasoning abilities of these models as a classification task pose numerous inaccuracies because of over-simplification. In this study, we built a psychological connection by bridging two disparate fields-human psychology and AI. We proposed an effective evaluation framework which can help to delineate the model's ethical reasoning ability in terms of moral consistency and Kohlberg's moral development stages with the help of Psychometric Assessment Tool-Defining Issues Test. |
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2023-09-26T00:00:00 | 2309.13308 | Calibrating LLM-Based Evaluator | [
"Yuxuan Liu",
"Tianchi Yang",
"Shaohan Huang",
"Zihan Zhang",
"Haizhen Huang",
"Furu Wei",
"Weiwei Deng",
"Feng Sun",
"Qi Zhang"
] | Recent advancements in large language models (LLMs) on language modeling and emergent capabilities make them a promising reference-free evaluator of natural language generation quality, and a competent alternative to human evaluation. However, hindered by the closed-source or high computational demand to host and tune, there is a lack of practice to further calibrate an off-the-shelf LLM-based evaluator towards better human alignment. In this work, we propose AutoCalibrate, a multi-stage, gradient-free approach to automatically calibrate and align an LLM-based evaluator toward human preference. Instead of explicitly modeling human preferences, we first implicitly encompass them within a set of human labels. Then, an initial set of scoring criteria is drafted by the language model itself, leveraging in-context learning on different few-shot examples. To further calibrate this set of criteria, we select the best performers and re-draft them with self-refinement. Our experiments on multiple text quality evaluation datasets illustrate a significant improvement in correlation with expert evaluation through calibration. Our comprehensive qualitative analysis conveys insightful intuitions and observations on the essence of effective scoring criteria. |
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2023-09-27T00:00:00 | 2309.15103 | LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models | [
"Yaohui Wang",
"Xinyuan Chen",
"Xin Ma",
"Shangchen Zhou",
"Ziqi Huang",
"Yi Wang",
"Ceyuan Yang",
"Yinan He",
"Jiashuo Yu",
"Peiqing Yang",
"Yuwei Guo",
"Tianxing Wu",
"Chenyang Si",
"Yuming Jiang",
"Cunjian Chen",
"Chen Change Loy",
"Bo Dai",
"Dahua Lin",
"Yu Qiao",
"Ziwei Liu"
] | https://github.com/Vchitect/LaVie | This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a pre-trained text-to-image (T2I) model as a basis. It is a highly desirable yet challenging task to simultaneously a) accomplish the synthesis of visually realistic and temporally coherent videos while b) preserving the strong creative generation nature of the pre-trained T2I model. To this end, we propose LaVie, an integrated video generation framework that operates on cascaded video latent diffusion models, comprising a base T2V model, a temporal interpolation model, and a video super-resolution model. Our key insights are two-fold: 1) We reveal that the incorporation of simple temporal self-attentions, coupled with rotary positional encoding, adequately captures the temporal correlations inherent in video data. 2) Additionally, we validate that the process of joint image-video fine-tuning plays a pivotal role in producing high-quality and creative outcomes. To enhance the performance of LaVie, we contribute a comprehensive and diverse video dataset named Vimeo25M, consisting of 25 million text-video pairs that prioritize quality, diversity, and aesthetic appeal. Extensive experiments demonstrate that LaVie achieves state-of-the-art performance both quantitatively and qualitatively. Furthermore, we showcase the versatility of pre-trained LaVie models in various long video generation and personalized video synthesis applications. |
2023-09-27T00:00:00 | 2309.14525 | Aligning Large Multimodal Models with Factually Augmented RLHF | [
"Zhiqing Sun",
"Sheng Shen",
"Shengcao Cao",
"Haotian Liu",
"Chunyuan Li",
"Yikang Shen",
"Chuang Gan",
"Liang-Yan Gui",
"Yu-Xiong Wang",
"Yiming Yang",
"Kurt Keutzer",
"Trevor Darrell"
] | Large Multimodal Models (LMM) are built across modalities and the misalignment between two modalities can result in "hallucination", generating textual outputs that are not grounded by the multimodal information in context. To address the multimodal misalignment issue, we adapt the Reinforcement Learning from Human Feedback (RLHF) from the text domain to the task of vision-language alignment, where human annotators are asked to compare two responses and pinpoint the more hallucinated one, and the vision-language model is trained to maximize the simulated human rewards. We propose a new alignment algorithm called Factually Augmented RLHF that augments the reward model with additional factual information such as image captions and ground-truth multi-choice options, which alleviates the reward hacking phenomenon in RLHF and further improves the performance. We also enhance the GPT-4-generated training data (for vision instruction tuning) with previously available human-written image-text pairs to improve the general capabilities of our model. To evaluate the proposed approach in real-world scenarios, we develop a new evaluation benchmark MMHAL-BENCH with a special focus on penalizing hallucinations. As the first LMM trained with RLHF, our approach achieves remarkable improvement on the LLaVA-Bench dataset with the 94% performance level of the text-only GPT-4 (while previous best methods can only achieve the 87% level), and an improvement by 60% on MMHAL-BENCH over other baselines. We opensource our code, model, data at https://llava-rlhf.github.io. |
|
2023-09-27T00:00:00 | 2309.14717 | QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models | [
"Yuhui Xu",
"Lingxi Xie",
"Xiaotao Gu",
"Xin Chen",
"Heng Chang",
"Hengheng Zhang",
"Zhensu Chen",
"Xiaopeng Zhang",
"Qi Tian"
] | https://github.com/yuhuixu1993/qa-lora | Recently years have witnessed a rapid development of large language models (LLMs). Despite the strong ability in many language-understanding tasks, the heavy computational burden largely restricts the application of LLMs especially when one needs to deploy them onto edge devices. In this paper, we propose a quantization-aware low-rank adaptation (QA-LoRA) algorithm. The motivation lies in the imbalanced degrees of freedom of quantization and adaptation, and the solution is to use group-wise operators which increase the degree of freedom of quantization meanwhile decreasing that of adaptation. QA-LoRA is easily implemented with a few lines of code, and it equips the original LoRA with two-fold abilities: (i) during fine-tuning, the LLM's weights are quantized (e.g., into INT4) to reduce time and memory usage; (ii) after fine-tuning, the LLM and auxiliary weights are naturally integrated into a quantized model without loss of accuracy. We apply QA-LoRA to the LLaMA and LLaMA2 model families and validate its effectiveness in different fine-tuning datasets and downstream scenarios. Code will be made available at https://github.com/yuhuixu1993/qa-lora. |
2023-09-27T00:00:00 | 2309.15098 | Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models | [
"Mert Yuksekgonul",
"Varun Chandrasekaran",
"Erik Jones",
"Suriya Gunasekar",
"Ranjita Naik",
"Hamid Palangi",
"Ece Kamar",
"Besmira Nushi"
] | We investigate the internal behavior of Transformer-based Large Language Models (LLMs) when they generate factually incorrect text. We propose modeling factual queries as Constraint Satisfaction Problems and use this framework to investigate how the model interacts internally with factual constraints. Specifically, we discover a strong positive relation between the model's attention to constraint tokens and the factual accuracy of its responses. In our curated suite of 11 datasets with over 40,000 prompts, we study the task of predicting factual errors with the Llama-2 family across all scales (7B, 13B, 70B). We propose SAT Probe, a method probing self-attention patterns, that can predict constraint satisfaction and factual errors, and allows early error identification. The approach and findings demonstrate how using the mechanistic understanding of factuality in LLMs can enhance reliability. |
|
2023-09-27T00:00:00 | 2309.14592 | Efficient Post-training Quantization with FP8 Formats | [
"Haihao Shen",
"Naveen Mellempudi",
"Xin He",
"Qun Gao",
"Chang Wang",
"Mengni Wang"
] | https://github.com/intel/neural-compressor | Recent advances in deep learning methods such as LLMs and Diffusion models have created a need for improved quantization methods that can meet the computational demands of these modern architectures while maintaining accuracy. Towards this goal, we study the advantages of FP8 data formats for post-training quantization across 75 unique network architectures covering a wide range of tasks, including machine translation, language modeling, text generation, image classification, generation, and segmentation. We examine three different FP8 representations (E5M2, E4M3, and E3M4) to study the effects of varying degrees of trade-off between dynamic range and precision on model accuracy. Based on our extensive study, we developed a quantization workflow that generalizes across different network architectures. Our empirical results show that FP8 formats outperform INT8 in multiple aspects, including workload coverage (92.64% vs. 65.87%), model accuracy and suitability for a broader range of operations. Furthermore, our findings suggest that E4M3 is better suited for NLP models, whereas E3M4 performs marginally better than E4M3 on computer vision tasks. The code is publicly available on Intel Neural Compressor: https://github.com/intel/neural-compressor. |
2023-09-27T00:00:00 | 2309.14509 | DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models | [
"Sam Ade Jacobs",
"Masahiro Tanaka",
"Chengming Zhang",
"Minjia Zhang",
"Leon Song",
"Samyam Rajbhandari",
"Yuxiong He"
] | Computation in a typical Transformer-based large language model (LLM) can be characterized by batch size, hidden dimension, number of layers, and sequence length. Until now, system works for accelerating LLM training have focused on the first three dimensions: data parallelism for batch size, tensor parallelism for hidden size and pipeline parallelism for model depth or layers. These widely studied forms of parallelism are not targeted or optimized for long sequence Transformer models. Given practical application needs for long sequence LLM, renewed attentions are being drawn to sequence parallelism. However, existing works in sequence parallelism are constrained by memory-communication inefficiency, limiting their scalability to long sequence large models. In this work, we introduce DeepSpeed-Ulysses, a novel, portable and effective methodology for enabling highly efficient and scalable LLM training with extremely long sequence length. DeepSpeed-Ulysses at its core partitions input data along the sequence dimension and employs an efficient all-to-all collective communication for attention computation. Theoretical communication analysis shows that whereas other methods incur communication overhead as sequence length increases, DeepSpeed-Ulysses maintains constant communication volume when sequence length and compute devices are increased proportionally. Furthermore, experimental evaluations show that DeepSpeed-Ulysses trains 2.5X faster with 4X longer sequence length than the existing method SOTA baseline. |
|
2023-09-27T00:00:00 | 2309.15091 | VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning | [
"Han Lin",
"Abhay Zala",
"Jaemin Cho",
"Mohit Bansal"
] | Although recent text-to-video (T2V) generation methods have seen significant advancements, most of these works focus on producing short video clips of a single event with a single background (i.e., single-scene videos). Meanwhile, recent large language models (LLMs) have demonstrated their capability in generating layouts and programs to control downstream visual modules such as image generation models. This raises an important question: can we leverage the knowledge embedded in these LLMs for temporally consistent long video generation? In this paper, we propose VideoDirectorGPT, a novel framework for consistent multi-scene video generation that uses the knowledge of LLMs for video content planning and grounded video generation. Specifically, given a single text prompt, we first ask our video planner LLM (GPT-4) to expand it into a 'video plan', which involves generating the scene descriptions, the entities with their respective layouts, the background for each scene, and consistency groupings of the entities and backgrounds. Next, guided by this output from the video planner, our video generator, Layout2Vid, has explicit control over spatial layouts and can maintain temporal consistency of entities/backgrounds across scenes, while only trained with image-level annotations. Our experiments demonstrate that VideoDirectorGPT framework substantially improves layout and movement control in both single- and multi-scene video generation and can generate multi-scene videos with visual consistency across scenes, while achieving competitive performance with SOTAs in open-domain single-scene T2V generation. We also demonstrate that our framework can dynamically control the strength for layout guidance and can also generate videos with user-provided images. We hope our framework can inspire future work on better integrating the planning ability of LLMs into consistent long video generation. |
|
2023-09-28T00:00:00 | 2309.15223 | Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition | [
"Yu Yu",
"Chao-Han Huck Yang",
"Jari Kolehmainen",
"Prashanth G. Shivakumar",
"Yile Gu",
"Sungho Ryu",
"Roger Ren",
"Qi Luo",
"Aditya Gourav",
"I-Fan Chen",
"Yi-Chieh Liu",
"Tuan Dinh",
"Ankur Gandhe",
"Denis Filimonov",
"Shalini Ghosh",
"Andreas Stolcke",
"Ariya Rastow",
"Ivan Bulyko"
] | We propose a neural language modeling system based on low-rank adaptation (LoRA) for speech recognition output rescoring. Although pretrained language models (LMs) like BERT have shown superior performance in second-pass rescoring, the high computational cost of scaling up the pretraining stage and adapting the pretrained models to specific domains limit their practical use in rescoring. Here we present a method based on low-rank decomposition to train a rescoring BERT model and adapt it to new domains using only a fraction (0.08%) of the pretrained parameters. These inserted matrices are optimized through a discriminative training objective along with a correlation-based regularization loss. The proposed low-rank adaptation Rescore-BERT (LoRB) architecture is evaluated on LibriSpeech and internal datasets with decreased training times by factors between 5.4 and 3.6. |
|
2023-09-28T00:00:00 | 2309.15251 | VPA: Fully Test-Time Visual Prompt Adaptation | [
"Jiachen Sun",
"Mark Ibrahim",
"Melissa Hall",
"Ivan Evtimov",
"Z. Morley Mao",
"Cristian Canton Ferrer",
"Caner Hazirbas"
] | Textual prompt tuning has demonstrated significant performance improvements in adapting natural language processing models to a variety of downstream tasks by treating hand-engineered prompts as trainable parameters. Inspired by the success of textual prompting, several studies have investigated the efficacy of visual prompt tuning. In this work, we present Visual Prompt Adaptation (VPA), the first framework that generalizes visual prompting with test-time adaptation. VPA introduces a small number of learnable tokens, enabling fully test-time and storage-efficient adaptation without necessitating source-domain information. We examine our VPA design under diverse adaptation settings, encompassing single-image, batched-image, and pseudo-label adaptation. We evaluate VPA on multiple tasks, including out-of-distribution (OOD) generalization, corruption robustness, and domain adaptation. Experimental results reveal that VPA effectively enhances OOD generalization by 3.3% across various models, surpassing previous test-time approaches. Furthermore, we show that VPA improves corruption robustness by 6.5% compared to strong baselines. Finally, we demonstrate that VPA also boosts domain adaptation performance by relatively 5.2%. Our VPA also exhibits marked effectiveness in improving the robustness of zero-shot recognition for vision-language models. |
|
2023-09-28T00:00:00 | 2309.15129 | Evaluating Cognitive Maps and Planning in Large Language Models with CogEval | [
"Ida Momennejad",
"Hosein Hasanbeig",
"Felipe Vieira",
"Hiteshi Sharma",
"Robert Osazuwa Ness",
"Nebojsa Jojic",
"Hamid Palangi",
"Jonathan Larson"
] | Recently an influx of studies claim emergent cognitive abilities in large language models (LLMs). Yet, most rely on anecdotes, overlook contamination of training sets, or lack systematic Evaluation involving multiple tasks, control conditions, multiple iterations, and statistical robustness tests. Here we make two major contributions. First, we propose CogEval, a cognitive science-inspired protocol for the systematic evaluation of cognitive capacities in Large Language Models. The CogEval protocol can be followed for the evaluation of various abilities. Second, here we follow CogEval to systematically evaluate cognitive maps and planning ability across eight LLMs (OpenAI GPT-4, GPT-3.5-turbo-175B, davinci-003-175B, Google Bard, Cohere-xlarge-52.4B, Anthropic Claude-1-52B, LLaMA-13B, and Alpaca-7B). We base our task prompts on human experiments, which offer both established construct validity for evaluating planning, and are absent from LLM training sets. We find that, while LLMs show apparent competence in a few planning tasks with simpler structures, systematic evaluation reveals striking failure modes in planning tasks, including hallucinations of invalid trajectories and getting trapped in loops. These findings do not support the idea of emergent out-of-the-box planning ability in LLMs. This could be because LLMs do not understand the latent relational structures underlying planning problems, known as cognitive maps, and fail at unrolling goal-directed trajectories based on the underlying structure. Implications for application and future directions are discussed. |
|
2023-09-28T00:00:00 | 2309.15807 | Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack | [
"Xiaoliang Dai",
"Ji Hou",
"Chih-Yao Ma",
"Sam Tsai",
"Jialiang Wang",
"Rui Wang",
"Peizhao Zhang",
"Simon Vandenhende",
"Xiaofang Wang",
"Abhimanyu Dubey",
"Matthew Yu",
"Abhishek Kadian",
"Filip Radenovic",
"Dhruv Mahajan",
"Kunpeng Li",
"Yue Zhao",
"Vladan Petrovic",
"Mitesh Kumar Singh",
"Simran Motwani",
"Yi Wen",
"Yiwen Song",
"Roshan Sumbaly",
"Vignesh Ramanathan",
"Zijian He",
"Peter Vajda",
"Devi Parikh"
] | Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text. However, these pre-trained models often face challenges when it comes to generating highly aesthetic images. This creates the need for aesthetic alignment post pre-training. In this paper, we propose quality-tuning to effectively guide a pre-trained model to exclusively generate highly visually appealing images, while maintaining generality across visual concepts. Our key insight is that supervised fine-tuning with a set of surprisingly small but extremely visually appealing images can significantly improve the generation quality. We pre-train a latent diffusion model on 1.1 billion image-text pairs and fine-tune it with only a few thousand carefully selected high-quality images. The resulting model, Emu, achieves a win rate of 82.9% compared with its pre-trained only counterpart. Compared to the state-of-the-art SDXLv1.0, Emu is preferred 68.4% and 71.3% of the time on visual appeal on the standard PartiPrompts and our Open User Input benchmark based on the real-world usage of text-to-image models. In addition, we show that quality-tuning is a generic approach that is also effective for other architectures, including pixel diffusion and masked generative transformer models. |
|
2023-09-28T00:00:00 | 2309.15818 | Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation | [
"David Junhao Zhang",
"Jay Zhangjie Wu",
"Jia-Wei Liu",
"Rui Zhao",
"Lingmin Ran",
"Yuchao Gu",
"Difei Gao",
"Mike Zheng Shou"
] | https://github.com/showlab/Show-1 | Significant advancements have been achieved in the realm of large-scale pre-trained text-to-video Diffusion Models (VDMs). However, previous methods either rely solely on pixel-based VDMs, which come with high computational costs, or on latent-based VDMs, which often struggle with precise text-video alignment. In this paper, we are the first to propose a hybrid model, dubbed as Show-1, which marries pixel-based and latent-based VDMs for text-to-video generation. Our model first uses pixel-based VDMs to produce a low-resolution video of strong text-video correlation. After that, we propose a novel expert translation method that employs the latent-based VDMs to further upsample the low-resolution video to high resolution. Compared to latent VDMs, Show-1 can produce high-quality videos of precise text-video alignment; Compared to pixel VDMs, Show-1 is much more efficient (GPU memory usage during inference is 15G vs 72G). We also validate our model on standard video generation benchmarks. Our code and model weights are publicly available at https://github.com/showlab/Show-1. |
2023-09-28T00:00:00 | 2309.15273 | DECO: Dense Estimation of 3D Human-Scene Contact In The Wild | [
"Shashank Tripathi",
"Agniv Chatterjee",
"Jean-Claude Passy",
"Hongwei Yi",
"Dimitrios Tzionas",
"Michael J. Black"
] | https://github.com/sha2nkt/deco | Understanding how humans use physical contact to interact with the world is key to enabling human-centric artificial intelligence. While inferring 3D contact is crucial for modeling realistic and physically-plausible human-object interactions, existing methods either focus on 2D, consider body joints rather than the surface, use coarse 3D body regions, or do not generalize to in-the-wild images. In contrast, we focus on inferring dense, 3D contact between the full body surface and objects in arbitrary images. To achieve this, we first collect DAMON, a new dataset containing dense vertex-level contact annotations paired with RGB images containing complex human-object and human-scene contact. Second, we train DECO, a novel 3D contact detector that uses both body-part-driven and scene-context-driven attention to estimate vertex-level contact on the SMPL body. DECO builds on the insight that human observers recognize contact by reasoning about the contacting body parts, their proximity to scene objects, and the surrounding scene context. We perform extensive evaluations of our detector on DAMON as well as on the RICH and BEHAVE datasets. We significantly outperform existing SOTA methods across all benchmarks. We also show qualitatively that DECO generalizes well to diverse and challenging real-world human interactions in natural images. The code, data, and models are available at https://deco.is.tue.mpg.de. |
2023-09-28T00:00:00 | 2309.15505 | Finite Scalar Quantization: VQ-VAE Made Simple | [
"Fabian Mentzer",
"David Minnen",
"Eirikur Agustsson",
"Michael Tschannen"
] | We propose to replace vector quantization (VQ) in the latent representation of VQ-VAEs with a simple scheme termed finite scalar quantization (FSQ), where we project the VAE representation down to a few dimensions (typically less than 10). Each dimension is quantized to a small set of fixed values, leading to an (implicit) codebook given by the product of these sets. By appropriately choosing the number of dimensions and values each dimension can take, we obtain the same codebook size as in VQ. On top of such discrete representations, we can train the same models that have been trained on VQ-VAE representations. For example, autoregressive and masked transformer models for image generation, multimodal generation, and dense prediction computer vision tasks. Concretely, we employ FSQ with MaskGIT for image generation, and with UViM for depth estimation, colorization, and panoptic segmentation. Despite the much simpler design of FSQ, we obtain competitive performance in all these tasks. We emphasize that FSQ does not suffer from codebook collapse and does not need the complex machinery employed in VQ (commitment losses, codebook reseeding, code splitting, entropy penalties, etc.) to learn expressive discrete representations. |
|
2023-09-28T00:00:00 | 2309.15426 | NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions | [
"Zhang Chen",
"Zhong Li",
"Liangchen Song",
"Lele Chen",
"Jingyi Yu",
"Junsong Yuan",
"Yi Xu"
] | https://github.com/oppo-us-research/NeuRBF | We present a novel type of neural fields that uses general radial bases for signal representation. State-of-the-art neural fields typically rely on grid-based representations for storing local neural features and N-dimensional linear kernels for interpolating features at continuous query points. The spatial positions of their neural features are fixed on grid nodes and cannot well adapt to target signals. Our method instead builds upon general radial bases with flexible kernel position and shape, which have higher spatial adaptivity and can more closely fit target signals. To further improve the channel-wise capacity of radial basis functions, we propose to compose them with multi-frequency sinusoid functions. This technique extends a radial basis to multiple Fourier radial bases of different frequency bands without requiring extra parameters, facilitating the representation of details. Moreover, by marrying adaptive radial bases with grid-based ones, our hybrid combination inherits both adaptivity and interpolation smoothness. We carefully designed weighting schemes to let radial bases adapt to different types of signals effectively. Our experiments on 2D image and 3D signed distance field representation demonstrate the higher accuracy and compactness of our method than prior arts. When applied to neural radiance field reconstruction, our method achieves state-of-the-art rendering quality, with small model size and comparable training speed. |
2023-09-28T00:00:00 | 2309.15564 | Jointly Training Large Autoregressive Multimodal Models | [
"Emanuele Aiello",
"Lili Yu",
"Yixin Nie",
"Armen Aghajanyan",
"Barlas Oguz"
] | In recent years, advances in the large-scale pretraining of language and text-to-image models have revolutionized the field of machine learning. Yet, integrating these two modalities into a single, robust model capable of generating seamless multimodal outputs remains a significant challenge. To address this gap, we present the Joint Autoregressive Mixture (JAM) framework, a modular approach that systematically fuses existing text and image generation models. We also introduce a specialized, data-efficient instruction-tuning strategy, tailored for mixed-modal generation tasks. Our final instruct-tuned model demonstrates unparalleled performance in generating high-quality multimodal outputs and represents the first model explicitly designed for this purpose. |
|
2023-09-29T00:00:00 | 2309.16609 | Qwen Technical Report | [
"Jinze Bai",
"Shuai Bai",
"Yunfei Chu",
"Zeyu Cui",
"Kai Dang",
"Xiaodong Deng",
"Yang Fan",
"Wenbin Ge",
"Yu Han",
"Fei Huang",
"Binyuan Hui",
"Luo Ji",
"Mei Li",
"Junyang Lin",
"Runji Lin",
"Dayiheng Liu",
"Gao Liu",
"Chengqiang Lu",
"Keming Lu",
"Jianxin Ma",
"Rui Men",
"Xingzhang Ren",
"Xuancheng Ren",
"Chuanqi Tan",
"Sinan Tan",
"Jianhong Tu",
"Peng Wang",
"Shijie Wang",
"Wei Wang",
"Shengguang Wu",
"Benfeng Xu",
"Jin Xu",
"An Yang",
"Hao Yang",
"Jian Yang",
"Shusheng Yang",
"Yang Yao",
"Bowen Yu",
"Hongyi Yuan",
"Zheng Yuan",
"Jianwei Zhang",
"Xingxuan Zhang",
"Yichang Zhang",
"Zhenru Zhang",
"Chang Zhou",
"Jingren Zhou",
"Xiaohuan Zhou",
"Tianhang Zhu"
] | https://github.com/QwenLM/Qwen | Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models. |
2023-09-29T00:00:00 | 2309.16668 | RealFill: Reference-Driven Generation for Authentic Image Completion | [
"Luming Tang",
"Nataniel Ruiz",
"Qinghao Chu",
"Yuanzhen Li",
"Aleksander Holynski",
"David E. Jacobs",
"Bharath Hariharan",
"Yael Pritch",
"Neal Wadhwa",
"Kfir Aberman",
"Michael Rubinstein"
] | Recent advances in generative imagery have brought forth outpainting and inpainting models that can produce high-quality, plausible image content in unknown regions, but the content these models hallucinate is necessarily inauthentic, since the models lack sufficient context about the true scene. In this work, we propose RealFill, a novel generative approach for image completion that fills in missing regions of an image with the content that should have been there. RealFill is a generative inpainting model that is personalized using only a few reference images of a scene. These reference images do not have to be aligned with the target image, and can be taken with drastically varying viewpoints, lighting conditions, camera apertures, or image styles. Once personalized, RealFill is able to complete a target image with visually compelling contents that are faithful to the original scene. We evaluate RealFill on a new image completion benchmark that covers a set of diverse and challenging scenarios, and find that it outperforms existing approaches by a large margin. See more results on our project page: https://realfill.github.io |
|
2023-09-29T00:00:00 | 2309.16534 | MotionLM: Multi-Agent Motion Forecasting as Language Modeling | [
"Ari Seff",
"Brian Cera",
"Dian Chen",
"Mason Ng",
"Aurick Zhou",
"Nigamaa Nayakanti",
"Khaled S. Refaat",
"Rami Al-Rfou",
"Benjamin Sapp"
] | Reliable forecasting of the future behavior of road agents is a critical component to safe planning in autonomous vehicles. Here, we represent continuous trajectories as sequences of discrete motion tokens and cast multi-agent motion prediction as a language modeling task over this domain. Our model, MotionLM, provides several advantages: First, it does not require anchors or explicit latent variable optimization to learn multimodal distributions. Instead, we leverage a single standard language modeling objective, maximizing the average log probability over sequence tokens. Second, our approach bypasses post-hoc interaction heuristics where individual agent trajectory generation is conducted prior to interactive scoring. Instead, MotionLM produces joint distributions over interactive agent futures in a single autoregressive decoding process. In addition, the model's sequential factorization enables temporally causal conditional rollouts. The proposed approach establishes new state-of-the-art performance for multi-agent motion prediction on the Waymo Open Motion Dataset, ranking 1st on the interactive challenge leaderboard. |
|
2023-09-29T00:00:00 | 2309.16414 | AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language Models | [
"Jan Hendrik Metzen",
"Piyapat Saranrittichai",
"Chaithanya Kumar Mummadi"
] | Classifiers built upon vision-language models such as CLIP have shown remarkable zero-shot performance across a broad range of image classification tasks. Prior work has studied different ways of automatically creating descriptor sets for every class based on prompt templates, ranging from manually engineered templates over templates obtained from a large language model to templates built from random words and characters. In contrast, deriving zero-shot classifiers from the respective encoded class descriptors has remained nearly unchanged, that is: classify to the class that maximizes the cosine similarity between its averaged encoded class descriptors and the encoded image. However, weighting all class descriptors equally can be suboptimal when certain descriptors match visual clues on a given image better than others. In this work, we propose AutoCLIP, a method for auto-tuning zero-shot classifiers. AutoCLIP assigns to each prompt template per-image weights, which are derived from statistics of class descriptor-image similarities at inference time. AutoCLIP is fully unsupervised, has very low overhead, and can be easily implemented in few lines of code. We show that for a broad range of vision-language models, datasets, and prompt templates, AutoCLIP outperforms baselines consistently and by up to 3 percent point accuracy. |
|
2023-09-29T00:00:00 | 2309.16058 | AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model | [
"Seungwhan Moon",
"Andrea Madotto",
"Zhaojiang Lin",
"Tushar Nagarajan",
"Matt Smith",
"Shashank Jain",
"Chun-Fu Yeh",
"Prakash Murugesan",
"Peyman Heidari",
"Yue Liu",
"Kavya Srinet",
"Babak Damavandi",
"Anuj Kumar"
] | We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i.e. text, image, video, audio, IMU motion sensor), and generates textual responses. AnyMAL inherits the powerful text-based reasoning abilities of the state-of-the-art LLMs including LLaMA-2 (70B), and converts modality-specific signals to the joint textual space through a pre-trained aligner module. To further strengthen the multimodal LLM's capabilities, we fine-tune the model with a multimodal instruction set manually collected to cover diverse topics and tasks beyond simple QAs. We conduct comprehensive empirical analysis comprising both human and automatic evaluations, and demonstrate state-of-the-art performance on various multimodal tasks. |
|
2023-09-29T00:00:00 | 2309.16039 | Effective Long-Context Scaling of Foundation Models | [
"Wenhan Xiong",
"Jingyu Liu",
"Igor Molybog",
"Hejia Zhang",
"Prajjwal Bhargava",
"Rui Hou",
"Louis Martin",
"Rashi Rungta",
"Karthik Abinav Sankararaman",
"Barlas Oguz",
"Madian Khabsa",
"Han Fang",
"Yashar Mehdad",
"Sharan Narang",
"Kshitiz Malik",
"Angela Fan",
"Shruti Bhosale",
"Sergey Edunov",
"Mike Lewis",
"Sinong Wang",
"Hao Ma"
] | We present a series of long-context LLMs that support effective context windows of up to 32,768 tokens. Our model series are built through continual pretraining from Llama 2 with longer training sequences and on a dataset where long texts are upsampled. We perform extensive evaluation on language modeling, synthetic context probing tasks, and a wide range of research benchmarks. On research benchmarks, our models achieve consistent improvements on most regular tasks and significant improvements on long-context tasks over Llama 2. Notably, with a cost-effective instruction tuning procedure that does not require human-annotated long instruction data, the 70B variant can already surpass gpt-3.5-turbo-16k's overall performance on a suite of long-context tasks. Alongside these results, we provide an in-depth analysis on the individual components of our method. We delve into Llama's position encodings and discuss its limitation in modeling long dependencies. We also examine the impact of various design choices in the pretraining process, including the data mix and the training curriculum of sequence lengths -- our ablation experiments suggest that having abundant long texts in the pretrain dataset is not the key to achieving strong performance, and we empirically verify that long context continual pretraining is more efficient and similarly effective compared to pretraining from scratch with long sequences. |
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2023-09-29T00:00:00 | 2309.16653 | DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation | [
"Jiaxiang Tang",
"Jiawei Ren",
"Hang Zhou",
"Ziwei Liu",
"Gang Zeng"
] | https://github.com/dreamgaussian/dreamgaussian | Recent advances in 3D content creation mostly leverage optimization-based 3D generation via score distillation sampling (SDS). Though promising results have been exhibited, these methods often suffer from slow per-sample optimization, limiting their practical usage. In this paper, we propose DreamGaussian, a novel 3D content generation framework that achieves both efficiency and quality simultaneously. Our key insight is to design a generative 3D Gaussian Splatting model with companioned mesh extraction and texture refinement in UV space. In contrast to the occupancy pruning used in Neural Radiance Fields, we demonstrate that the progressive densification of 3D Gaussians converges significantly faster for 3D generative tasks. To further enhance the texture quality and facilitate downstream applications, we introduce an efficient algorithm to convert 3D Gaussians into textured meshes and apply a fine-tuning stage to refine the details. Extensive experiments demonstrate the superior efficiency and competitive generation quality of our proposed approach. Notably, DreamGaussian produces high-quality textured meshes in just 2 minutes from a single-view image, achieving approximately 10 times acceleration compared to existing methods. |
2023-09-29T00:00:00 | 2309.16585 | Text-to-3D using Gaussian Splatting | [
"Zilong Chen",
"Feng Wang",
"Huaping Liu"
] | https://github.com/gsgen3d/gsgen | In this paper, we present Gaussian Splatting based text-to-3D generation (GSGEN), a novel approach for generating high-quality 3D objects. Previous methods suffer from inaccurate geometry and limited fidelity due to the absence of 3D prior and proper representation. We leverage 3D Gaussian Splatting, a recent state-of-the-art representation, to address existing shortcomings by exploiting the explicit nature that enables the incorporation of 3D prior. Specifically, our method adopts a progressive optimization strategy, which includes a geometry optimization stage and an appearance refinement stage. In geometry optimization, a coarse representation is established under a 3D geometry prior along with the ordinary 2D SDS loss, ensuring a sensible and 3D-consistent rough shape. Subsequently, the obtained Gaussians undergo an iterative refinement to enrich details. In this stage, we increase the number of Gaussians by compactness-based densification to enhance continuity and improve fidelity. With these designs, our approach can generate 3D content with delicate details and more accurate geometry. Extensive evaluations demonstrate the effectiveness of our method, especially for capturing high-frequency components. Video results are provided at https://gsgen3d.github.io. Our code is available at https://github.com/gsgen3d/gsgen |
2023-09-29T00:00:00 | 2309.16429 | Diverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptation | [
"Guy Yariv",
"Itai Gat",
"Sagie Benaim",
"Lior Wolf",
"Idan Schwartz",
"Yossi Adi"
] | We consider the task of generating diverse and realistic videos guided by natural audio samples from a wide variety of semantic classes. For this task, the videos are required to be aligned both globally and temporally with the input audio: globally, the input audio is semantically associated with the entire output video, and temporally, each segment of the input audio is associated with a corresponding segment of that video. We utilize an existing text-conditioned video generation model and a pre-trained audio encoder model. The proposed method is based on a lightweight adaptor network, which learns to map the audio-based representation to the input representation expected by the text-to-video generation model. As such, it also enables video generation conditioned on text, audio, and, for the first time as far as we can ascertain, on both text and audio. We validate our method extensively on three datasets demonstrating significant semantic diversity of audio-video samples and further propose a novel evaluation metric (AV-Align) to assess the alignment of generated videos with input audio samples. AV-Align is based on the detection and comparison of energy peaks in both modalities. In comparison to recent state-of-the-art approaches, our method generates videos that are better aligned with the input sound, both with respect to content and temporal axis. We also show that videos produced by our method present higher visual quality and are more diverse. |
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2023-09-29T00:00:00 | 2309.16496 | CCEdit: Creative and Controllable Video Editing via Diffusion Models | [
"Ruoyu Feng",
"Wenming Weng",
"Yanhui Wang",
"Yuhui Yuan",
"Jianmin Bao",
"Chong Luo",
"Zhibo Chen",
"Baining Guo"
] | In this work, we present CCEdit, a versatile framework designed to address the challenges of creative and controllable video editing. CCEdit accommodates a wide spectrum of user editing requirements and enables enhanced creative control through an innovative approach that decouples video structure and appearance. We leverage the foundational ControlNet architecture to preserve structural integrity, while seamlessly integrating adaptable temporal modules compatible with state-of-the-art personalization techniques for text-to-image generation, such as DreamBooth and LoRA.Furthermore, we introduce reference-conditioned video editing, empowering users to exercise precise creative control over video editing through the more manageable process of editing key frames. Our extensive experimental evaluations confirm the exceptional functionality and editing capabilities of the proposed CCEdit framework. Demo video is available at https://www.youtube.com/watch?v=UQw4jq-igN4. |
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2023-09-29T00:00:00 | 2309.16235 | Language models in molecular discovery | [
"Nikita Janakarajan",
"Tim Erdmann",
"Sarath Swaminathan",
"Teodoro Laino",
"Jannis Born"
] | The success of language models, especially transformer-based architectures, has trickled into other domains giving rise to "scientific language models" that operate on small molecules, proteins or polymers. In chemistry, language models contribute to accelerating the molecule discovery cycle as evidenced by promising recent findings in early-stage drug discovery. Here, we review the role of language models in molecular discovery, underlining their strength in de novo drug design, property prediction and reaction chemistry. We highlight valuable open-source software assets thus lowering the entry barrier to the field of scientific language modeling. Last, we sketch a vision for future molecular design that combines a chatbot interface with access to computational chemistry tools. Our contribution serves as a valuable resource for researchers, chemists, and AI enthusiasts interested in understanding how language models can and will be used to accelerate chemical discovery. |
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2023-09-29T00:00:00 | 2309.16650 | ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning | [
"Qiao Gu",
"Alihusein Kuwajerwala",
"Sacha Morin",
"Krishna Murthy Jatavallabhula",
"Bipasha Sen",
"Aditya Agarwal",
"Corban Rivera",
"William Paul",
"Kirsty Ellis",
"Rama Chellappa",
"Chuang Gan",
"Celso Miguel de Melo",
"Joshua B. Tenenbaum",
"Antonio Torralba",
"Florian Shkurti",
"Liam Paull"
] | For robots to perform a wide variety of tasks, they require a 3D representation of the world that is semantically rich, yet compact and efficient for task-driven perception and planning. Recent approaches have attempted to leverage features from large vision-language models to encode semantics in 3D representations. However, these approaches tend to produce maps with per-point feature vectors, which do not scale well in larger environments, nor do they contain semantic spatial relationships between entities in the environment, which are useful for downstream planning. In this work, we propose ConceptGraphs, an open-vocabulary graph-structured representation for 3D scenes. ConceptGraphs is built by leveraging 2D foundation models and fusing their output to 3D by multi-view association. The resulting representations generalize to novel semantic classes, without the need to collect large 3D datasets or finetune models. We demonstrate the utility of this representation through a number of downstream planning tasks that are specified through abstract (language) prompts and require complex reasoning over spatial and semantic concepts. (Project page: https://concept-graphs.github.io/ Explainer video: https://youtu.be/mRhNkQwRYnc ) |
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2023-09-29T00:00:00 | 2309.16583 | GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond | [
"Shen Zheng",
"Yuyu Zhang",
"Yijie Zhu",
"Chenguang Xi",
"Pengyang Gao",
"Xun Zhou",
"Kevin Chen-Chuan Chang"
] | With the rapid advancement of large language models (LLMs), there is a pressing need for a comprehensive evaluation suite to assess their capabilities and limitations. Existing LLM leaderboards often reference scores reported in other papers without consistent settings and prompts, which may inadvertently encourage cherry-picking favored settings and prompts for better results. In this work, we introduce GPT-Fathom, an open-source and reproducible LLM evaluation suite built on top of OpenAI Evals. We systematically evaluate 10+ leading LLMs as well as OpenAI's legacy models on 20+ curated benchmarks across 7 capability categories, all under aligned settings. Our retrospective study on OpenAI's earlier models offers valuable insights into the evolutionary path from GPT-3 to GPT-4. Currently, the community is eager to know how GPT-3 progressively improves to GPT-4, including technical details like whether adding code data improves LLM's reasoning capability, which aspects of LLM capability can be improved by SFT and RLHF, how much is the alignment tax, etc. Our analysis sheds light on many of these questions, aiming to improve the transparency of advanced LLMs. |
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2023-09-29T00:00:00 | 2309.16671 | Demystifying CLIP Data | [
"Hu Xu",
"Saining Xie",
"Xiaoqing Ellen Tan",
"Po-Yao Huang",
"Russell Howes",
"Vasu Sharma",
"Shang-Wen Li",
"Gargi Ghosh",
"Luke Zettlemoyer",
"Christoph Feichtenhofer"
] | https://github.com/facebookresearch/MetaCLIP | Contrastive Language-Image Pre-training (CLIP) is an approach that has advanced research and applications in computer vision, fueling modern recognition systems and generative models. We believe that the main ingredient to the success of CLIP is its data and not the model architecture or pre-training objective. However, CLIP only provides very limited information about its data and how it has been collected, leading to works that aim to reproduce CLIP's data by filtering with its model parameters. In this work, we intend to reveal CLIP's data curation approach and in our pursuit of making it open to the community introduce Metadata-Curated Language-Image Pre-training (MetaCLIP). MetaCLIP takes a raw data pool and metadata (derived from CLIP's concepts) and yields a balanced subset over the metadata distribution. Our experimental study rigorously isolates the model and training settings, concentrating solely on data. MetaCLIP applied to CommonCrawl with 400M image-text data pairs outperforms CLIP's data on multiple standard benchmarks. In zero-shot ImageNet classification, MetaCLIP achieves 70.8% accuracy, surpassing CLIP's 68.3% on ViT-B models. Scaling to 1B data, while maintaining the same training budget, attains 72.4%. Our observations hold across various model sizes, exemplified by ViT-H achieving 80.5%, without any bells-and-whistles. Curation code and training data distribution on metadata is made available at https://github.com/facebookresearch/MetaCLIP. |
2023-09-29T00:00:00 | 2309.16588 | Vision Transformers Need Registers | [
"Timothée Darcet",
"Maxime Oquab",
"Julien Mairal",
"Piotr Bojanowski"
] | Transformers have recently emerged as a powerful tool for learning visual representations. In this paper, we identify and characterize artifacts in feature maps of both supervised and self-supervised ViT networks. The artifacts correspond to high-norm tokens appearing during inference primarily in low-informative background areas of images, that are repurposed for internal computations. We propose a simple yet effective solution based on providing additional tokens to the input sequence of the Vision Transformer to fill that role. We show that this solution fixes that problem entirely for both supervised and self-supervised models, sets a new state of the art for self-supervised visual models on dense visual prediction tasks, enables object discovery methods with larger models, and most importantly leads to smoother feature maps and attention maps for downstream visual processing. |
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2023-09-29T00:00:00 | 2309.16643 | Deep Geometrized Cartoon Line Inbetweening | [
"Li Siyao",
"Tianpei Gu",
"Weiye Xiao",
"Henghui Ding",
"Ziwei Liu",
"Chen Change Loy"
] | https://github.com/lisiyao21/AnimeInbet | We aim to address a significant but understudied problem in the anime industry, namely the inbetweening of cartoon line drawings. Inbetweening involves generating intermediate frames between two black-and-white line drawings and is a time-consuming and expensive process that can benefit from automation. However, existing frame interpolation methods that rely on matching and warping whole raster images are unsuitable for line inbetweening and often produce blurring artifacts that damage the intricate line structures. To preserve the precision and detail of the line drawings, we propose a new approach, AnimeInbet, which geometrizes raster line drawings into graphs of endpoints and reframes the inbetweening task as a graph fusion problem with vertex repositioning. Our method can effectively capture the sparsity and unique structure of line drawings while preserving the details during inbetweening. This is made possible via our novel modules, i.e., vertex geometric embedding, a vertex correspondence Transformer, an effective mechanism for vertex repositioning and a visibility predictor. To train our method, we introduce MixamoLine240, a new dataset of line drawings with ground truth vectorization and matching labels. Our experiments demonstrate that AnimeInbet synthesizes high-quality, clean, and complete intermediate line drawings, outperforming existing methods quantitatively and qualitatively, especially in cases with large motions. Data and code are available at https://github.com/lisiyao21/AnimeInbet. |
2023-10-03T00:00:00 | 2310.00426 | PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis | [
"Junsong Chen",
"Jincheng Yu",
"Chongjian Ge",
"Lewei Yao",
"Enze Xie1",
"Yue Wu",
"Zhongdao Wang",
"James Kwok",
"Ping Luo",
"Huchuan Lu",
"Zhenguo Li"
] | https://github.com/PixArt-alpha/PixArt-alpha | The most advanced text-to-image (T2I) models require significant training costs (e.g., millions of GPU hours), seriously hindering the fundamental innovation for the AIGC community while increasing CO2 emissions. This paper introduces PIXART-alpha, a Transformer-based T2I diffusion model whose image generation quality is competitive with state-of-the-art image generators (e.g., Imagen, SDXL, and even Midjourney), reaching near-commercial application standards. Additionally, it supports high-resolution image synthesis up to 1024px resolution with low training cost, as shown in Figure 1 and 2. To achieve this goal, three core designs are proposed: (1) Training strategy decomposition: We devise three distinct training steps that separately optimize pixel dependency, text-image alignment, and image aesthetic quality; (2) Efficient T2I Transformer: We incorporate cross-attention modules into Diffusion Transformer (DiT) to inject text conditions and streamline the computation-intensive class-condition branch; (3) High-informative data: We emphasize the significance of concept density in text-image pairs and leverage a large Vision-Language model to auto-label dense pseudo-captions to assist text-image alignment learning. As a result, PIXART-alpha's training speed markedly surpasses existing large-scale T2I models, e.g., PIXART-alpha only takes 10.8% of Stable Diffusion v1.5's training time (675 vs. 6,250 A100 GPU days), saving nearly \300,000 (26,000 vs. \320,000) and reducing 90% CO2 emissions. Moreover, compared with a larger SOTA model, RAPHAEL, our training cost is merely 1%. Extensive experiments demonstrate that PIXART-\alpha excels in image quality, artistry, and semantic control. We hope PIXART-\alpha$ will provide new insights to the AIGC community and startups to accelerate building their own high-quality yet low-cost generative models from scratch. |
2023-10-03T00:00:00 | 2310.00898 | Enable Language Models to Implicitly Learn Self-Improvement From Data | [
"Ziqi Wang",
"Le Hou",
"Tianjian Lu",
"Yuexin Wu",
"Yunxuan Li",
"Hongkun Yu",
"Heng Ji"
] | Large Language Models (LLMs) have demonstrated remarkable capabilities in open-ended text generation tasks. However, the inherent open-ended nature of these tasks implies that there is always room for improvement in the quality of model responses. To address this challenge, various approaches have been proposed to enhance the performance of LLMs. There has been a growing focus on enabling LLMs to self-improve their response quality, thereby reducing the reliance on extensive human annotation efforts for collecting diverse and high-quality training data. Recently, prompting-based methods have been widely explored among self-improvement methods owing to their effectiveness, efficiency, and convenience. However, those methods usually require explicitly and thoroughly written rubrics as inputs to LLMs. It is expensive and challenging to manually derive and provide all necessary rubrics with a real-world complex goal for improvement (e.g., being more helpful and less harmful). To this end, we propose an ImPlicit Self-ImprovemenT (PIT) framework that implicitly learns the improvement goal from human preference data. PIT only requires preference data that are used to train reward models without extra human efforts. Specifically, we reformulate the training objective of reinforcement learning from human feedback (RLHF) -- instead of maximizing response quality for a given input, we maximize the quality gap of the response conditioned on a reference response. In this way, PIT is implicitly trained with the improvement goal of better aligning with human preferences. Experiments on two real-world datasets and one synthetic dataset show that our method significantly outperforms prompting-based methods. |
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