Upload folder using huggingface_hub

0.codes.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f47e725caa3a0b701d340731857257e7bcac229844161271145e8997f54dd504
-size 2851740
+oid sha256:c95e6385e2a1184e7e26b18ae6e13df500d6a25fafcfca5ed7af507405e250c6
+size 2853020

0.metadata.json

@@ -1,6 +1,6 @@
 {
     "passage_offset": 0,
-    "num_passages": 4152,
-    "num_embeddings": 712645,
+    "num_passages": 4154,
+    "num_embeddings": 712962,
     "embedding_offset": 0
 }

0.residuals.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5c6cfefe0d6b1472085a1069409c790bcb29ed4557578c3f2f29b8ebd7826b04
-size 91219760
+oid sha256:fb2a878ffb3c7de100f06795f540326dd165e8eecae32560d0eb3b64c5de64e8
+size 91260336

avg_residual.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:84d16c018087eb3fa7d793f5b3f7928032339aef58194fc2c02f1574b768daab
+oid sha256:c8e23c5c34cd610d331c5643c0467bc704dab808753767a6eb9e61d7e8239439
 size 1205

buckets.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:041d1ab36be8d21dced0fa9e39c48194bc2ef73ddc2980d2f96a27cafaca11fc
+oid sha256:664330a9b0b8bd32d7749e37c28a27ebfdb3b567bc7113eb602789d8b0a6006b
 size 2904

centroids.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:383e2dca85e447f26e9ad11f34bd1f441f46ccaa38e33abae370c1dd84e373bf
+oid sha256:4a96e59cdfc3b9e66700268c74bbabff8eba738c2109ef9636516dd4a0999494
 size 2098342

collection.json

@@ -4150,5 +4150,7 @@
   "The computational challenges of Large Language Model (LLM) inference remain a significant barrier to their widespread deployment, especially as prompt lengths continue to increase. Due to the quadratic complexity of the attention computation, it takes 30 minutes for an 8B LLM to process a prompt of 1M tokens (i.e., the pre-filling stage) on a single A100 GPU. Existing methods for speeding up prefilling often fail to maintain acceptable accuracy or efficiency when applied to long-context LLMs. To address this gap, we introduce MInference (Milliontokens Inference), a sparse calculation method designed to accelerate pre-filling of long-sequence processing. Specifically, we identify three unique patterns in long-context attention matrices-the A-shape, Vertical-Slash, and Block-Sparsethat can be leveraged for efficient sparse computation on GPUs. We determine the optimal pattern for each attention head offline and dynamically build sparse indices based on the assigned pattern during inference. With the pattern and sparse indices, we perform efficient sparse attention calculations via our optimized GPU kernels to significantly reduce the latency in the pre-filling stage of long-context LLMs.",
   "We determine the optimal pattern for each attention head offline and dynamically build sparse indices based on the assigned pattern during inference. With the pattern and sparse indices, we perform efficient sparse attention calculations via our optimized GPU kernels to significantly reduce the latency in the pre-filling stage of long-context LLMs. Our proposed technique can be directly applied to existing LLMs without any modifications to the pre-training setup or additional fine-tuning. By evaluating on a wide range of downstream tasks, including InfiniteBench, RULER, PG-19, and Needle In A Haystack, and models including LLaMA-3-1M, GLM4-1M, Yi-200K, Phi-3-128K, and Qwen2-128K, we demonstrate that MInference effectively reduces inference latency by up to 10x for pre-filling on an A100, while maintaining accuracy. Our code is available at https://aka.ms/MInference.",
   "Recent advancements in large language models (LLMs) have significantly advanced the automation of software development tasks, including code synthesis, program repair, and test generation. More recently, researchers and industry practitioners have developed various autonomous LLM agents to perform end-to-end software development tasks. These agents are equipped with the ability to use tools, run commands, observe feedback from the environment, and plan for future actions. However, the complexity of these agent-based approaches, together with the limited abilities of current LLMs, raises the following question: Do we really have to employ complex autonomous software agents? To attempt to answer this question, we build Agentless -- an agentless approach to automatically solve software development problems. Compared to the verbose and complex setup of agent-based approaches, Agentless employs a simplistic two-phase process of localization followed by repair, without letting the LLM decide future actions or operate with complex tools. Our results on the popular SWE-bench Lite benchmark show that surprisingly the simplistic Agentless is able to achieve both the highest performance (27.33%) and lowest cost (\\$0.34) compared with all existing open-source software agents!",
-  "Our results on the popular SWE-bench Lite benchmark show that surprisingly the simplistic Agentless is able to achieve both the highest performance (27.33%) and lowest cost (\\$0.34) compared with all existing open-source software agents! Furthermore, we manually classified the problems in SWE-bench Lite and found problems with exact ground truth patch or insufficient/misleading issue descriptions. As such, we construct SWE-bench Lite-S by excluding such problematic issues to perform more rigorous evaluation and comparison. Our work highlights the current overlooked potential of a simple, interpretable technique in autonomous software development. We hope Agentless will help reset the baseline, starting point, and horizon for autonomous software agents, and inspire future work along this crucial direction."
+  "Our results on the popular SWE-bench Lite benchmark show that surprisingly the simplistic Agentless is able to achieve both the highest performance (27.33%) and lowest cost (\\$0.34) compared with all existing open-source software agents! Furthermore, we manually classified the problems in SWE-bench Lite and found problems with exact ground truth patch or insufficient/misleading issue descriptions. As such, we construct SWE-bench Lite-S by excluding such problematic issues to perform more rigorous evaluation and comparison. Our work highlights the current overlooked potential of a simple, interpretable technique in autonomous software development. We hope Agentless will help reset the baseline, starting point, and horizon for autonomous software agents, and inspire future work along this crucial direction.",
+  "LLMs and RAG systems are now capable of handling millions of input tokens or more. However, evaluating the output quality of such systems on long-context tasks remains challenging, as tasks like Needle-in-a-Haystack lack complexity. In this work, we argue that summarization can play a central role in such evaluation. We design a procedure to synthesize Haystacks of documents, ensuring that specific insights repeat across documents. The \"Summary of a Haystack\" (SummHay) task then requires a system to process the Haystack and generate, given a query, a summary that identifies the relevant insights and precisely cites the source documents. Since we have precise knowledge of what insights should appear in a haystack summary and what documents should be cited, we implement a highly reproducible automatic evaluation that can score summaries on two aspects - Coverage and Citation. We generate Haystacks in two domains (conversation, news), and perform a large-scale evaluation of 10 LLMs and corresponding 50 RAG systems.",
+  "We generate Haystacks in two domains (conversation, news), and perform a large-scale evaluation of 10 LLMs and corresponding 50 RAG systems. Our findings indicate that SummHay is an open challenge for current systems, as even systems provided with an Oracle signal of document relevance lag our estimate of human performance (56\\%) by 10+ points on a Joint Score. Without a retriever, long-context LLMs like GPT-4o and Claude 3 Opus score below 20% on SummHay. We show SummHay can also be used to study enterprise RAG systems and position bias in long-context models. We hope future systems can equal and surpass human performance on SummHay."
 ]

doclens.0.json

@@ -1 +1 @@
-[178,205,218,148,184,163,221,185,200,228,172,155,210,222,88,206,226,67,132,212,91,206,104,212,174,205,132,159,230,175,216,198,227,190,212,198,122,213,169,204,92,197,118,191,191,224,69,219,197,72,218,77,175,111,155,217,220,170,231,91,221,217,95,146,177,123,195,205,151,209,207,36,202,200,226,176,232,53,167,199,89,184,213,104,154,153,216,214,215,174,205,72,211,78,221,212,232,223,73,158,220,158,202,222,189,165,205,175,222,132,126,179,219,110,209,158,208,98,176,192,226,34,158,205,126,178,224,182,227,100,152,191,169,195,163,172,208,117,199,217,167,217,157,163,194,217,200,217,23,221,209,146,150,204,200,125,215,232,68,147,212,41,223,178,152,173,210,139,198,182,196,207,95,176,205,223,83,216,85,207,210,52,177,178,230,197,119,226,99,182,210,212,77,138,199,123,179,111,219,69,223,65,204,215,83,197,87,211,132,216,135,178,157,166,216,85,170,195,208,190,175,134,220,200,67,221,211,66,227,222,226,190,209,205,67,207,139,208,127,186,205,168,221,179,223,117,148,221,216,80,189,125,199,202,64,218,77,195,190,221,181,98,143,214,220,97,187,127,219,122,216,87,138,212,194,112,219,227,101,220,100,164,234,109,221,102,223,89,184,205,219,77,188,223,172,171,175,152,175,137,213,197,114,205,221,138,181,174,227,73,147,144,178,147,215,152,182,204,80,210,123,211,121,209,224,224,219,211,163,133,187,148,151,163,221,94,133,213,72,187,224,216,162,154,224,184,118,204,220,154,117,220,162,202,223,195,110,197,151,224,88,182,217,221,214,118,218,118,164,205,97,221,183,154,206,197,74,170,219,103,230,215,192,224,78,184,72,201,227,221,191,181,104,190,224,221,99,123,206,102,228,202,74,195,96,225,176,232,231,96,225,206,61,173,101,190,211,90,213,199,203,184,209,173,160,207,203,99,201,195,132,195,214,148,211,227,192,215,212,127,162,213,114,178,111,207,71,129,182,212,75,176,209,137,213,224,220,70,232,116,228,179,200,184,222,93,202,71,219,123,213,119,204,173,135,118,207,96,216,107,210,202,189,172,211,76,146,195,169,83,227,62,219,223,125,158,202,226,98,214,225,76,156,211,204,110,190,224,200,64,228,223,72,213,102,214,178,219,128,211,127,187,213,160,216,134,202,150,186,201,195,230,199,206,193,205,219,216,84,213,223,192,222,109,187,209,79,153,204,224,72,167,190,183,183,228,133,227,103,197,225,203,205,201,207,213,210,78,138,195,133,195,180,190,185,207,189,100,191,229,198,129,226,135,205,164,226,190,73,200,231,209,216,147,221,228,165,213,209,181,157,222,117,216,58,148,219,92,222,219,63,191,218,186,197,158,172,204,109,204,102,223,229,91,192,217,186,204,144,173,209,95,186,222,225,178,183,222,71,193,182,232,63,227,228,82,209,162,219,183,222,122,212,195,60,219,130,223,69,216,111,198,224,80,223,74,164,148,227,193,216,139,207,198,148,227,199,169,230,225,86,138,165,206,91,213,164,215,229,173,216,210,92,166,205,102,196,78,202,74,187,202,229,191,222,57,206,115,183,94,225,173,198,109,212,224,126,215,44,221,80,196,95,177,218,217,198,109,218,191,221,214,227,71,207,123,206,90,219,218,108,181,214,123,150,204,214,208,225,199,191,192,213,93,217,149,178,199,106,204,80,208,60,131,132,232,218,213,115,202,94,175,227,217,114,200,222,178,180,185,192,193,205,173,226,217,222,197,184,205,211,136,214,221,62,213,89,169,184,135,198,98,216,225,71,230,133,213,154,216,189,122,224,108,204,88,207,227,93,191,204,214,66,153,219,209,203,218,237,229,222,148,174,218,107,214,169,223,216,173,220,148,199,188,175,203,142,188,210,98,167,174,224,103,156,97,220,111,216,70,130,221,216,115,166,164,162,210,216,69,173,203,77,216,175,172,210,98,199,213,88,221,82,184,131,201,220,133,211,116,156,219,213,227,128,221,237,172,217,64,180,221,70,169,220,216,134,215,177,190,197,229,221,226,74,189,190,219,86,222,74,213,106,184,182,205,221,229,143,201,198,162,209,128,221,209,60,216,146,228,106,215,154,214,202,82,194,141,168,217,213,80,140,114,220,149,229,215,150,211,170,199,109,218,108,206,125,204,164,183,148,193,153,188,143,160,199,210,124,225,105,178,75,222,112,205,75,199,83,222,215,158,104,205,70,215,223,158,220,99,124,194,91,219,65,209,149,234,188,222,218,200,66,208,101,208,188,201,207,198,160,205,219,118,170,195,141,128,182,189,211,51,179,216,112,152,196,224,80,223,177,218,127,186,113,151,117,159,184,200,231,212,134,222,68,216,204,90,151,202,158,173,146,144,209,222,89,171,188,206,215,117,191,223,80,215,68,151,159,210,74,208,105,204,105,174,228,67,147,202,214,41,222,163,183,142,227,188,200,208,132,157,191,195,94,166,201,155,214,132,185,195,165,214,219,156,217,100,147,220,105,220,223,225,117,176,166,207,134,218,202,172,183,217,76,229,139,217,87,208,181,214,169,154,199,231,208,200,157,213,222,49,207,191,139,219,218,82,122,154,137,190,176,192,180,231,80,208,136,185,112,189,187,192,208,219,110,208,102,186,225,220,218,235,214,96,133,198,199,95,152,83,219,190,124,148,222,213,207,145,217,168,200,32,208,198,171,205,218,104,218,136,230,77,139,178,212,214,129,211,233,93,221,95,155,153,221,151,201,108,193,216,172,208,72,177,217,208,225,60,81,172,234,180,146,184,180,199,222,224,182,106,183,105,221,208,81,221,137,211,44,198,224,205,82,187,164,147,213,163,229,192,219,218,190,228,221,218,116,231,97,182,126,213,193,173,207,103,207,122,161,156,193,153,192,92,198,155,223,224,106,223,170,177,225,40,206,203,221,152,208,116,203,105,197,221,65,226,111,224,215,215,142,154,211,84,184,135,169,212,213,76,229,225,130,207,85,209,177,116,140,150,229,209,105,164,186,214,219,62,148,203,230,118,207,216,217,211,114,201,134,217,87,197,124,209,186,184,164,189,163,212,85,219,106,209,220,168,221,212,112,189,183,229,200,192,106,224,114,220,95,141,223,222,219,226,79,187,202,212,222,193,128,223,209,160,178,197,222,111,152,114,164,206,179,189,183,122,212,149,150,130,214,115,195,118,204,208,120,195,163,159,220,125,222,174,211,144,140,220,80,173,204,87,215,149,227,97,229,221,119,213,139,212,82,215,68,213,136,186,168,202,85,213,93,168,218,63,222,121,213,90,218,94,197,221,182,220,112,224,221,83,215,194,138,181,197,221,91,186,194,99,179,213,210,76,188,214,216,79,213,92,220,94,201,108,213,131,221,153,219,74,159,172,218,217,219,108,209,35,217,112,157,206,148,211,126,214,107,191,105,164,224,220,126,204,201,211,119,203,129,165,205,130,218,137,211,178,204,215,206,119,209,75,193,182,115,178,207,217,220,184,199,167,189,230,140,208,167,173,195,136,220,205,64,186,159,194,75,213,189,110,189,208,120,176,182,162,196,183,137,134,198,172,193,184,172,212,189,214,200,95,167,199,110,195,116,180,204,66,217,126,222,226,193,166,143,121,218,224,196,140,226,142,206,66,214,140,218,144,201,221,152,204,154,221,101,225,69,216,212,128,181,200,180,206,136,193,226,226,219,141,168,202,209,105,178,128,208,197,145,163,218,124,220,147,220,156,211,69,212,226,132,223,165,211,110,229,194,193,204,208,90,218,106,152,215,134,180,182,204,211,80,211,108,204,102,208,102,192,94,191,206,203,140,165,213,197,105,186,186,210,62,208,165,213,174,186,115,211,164,193,128,201,169,217,167,217,174,222,167,129,170,217,218,182,159,113,208,85,219,126,219,89,172,213,212,69,205,135,124,205,67,208,52,186,160,225,125,190,65,227,168,180,166,151,194,205,88,138,204,110,124,223,80,193,106,179,183,163,220,148,205,218,200,221,198,202,190,213,87,227,239,81,213,115,211,219,201,204,105,216,83,210,95,170,219,197,86,228,86,209,125,231,83,198,194,171,144,183,222,216,216,226,204,109,216,115,180,227,116,207,223,84,190,215,117,212,79,214,230,218,134,208,92,209,99,181,220,61,212,85,199,185,174,171,220,221,57,146,224,214,81,183,218,152,163,193,72,203,111,130,222,204,217,229,209,201,189,220,213,72,192,214,221,157,197,124,209,100,223,162,223,73,85,221,68,210,46,205,76,118,160,180,200,85,218,34,196,170,204,88,168,224,71,188,207,166,181,210,101,148,215,76,219,217,104,199,180,210,208,69,201,202,216,224,89,206,88,128,125,196,203,106,183,182,84,228,200,120,171,166,153,215,210,82,210,65,163,131,213,83,180,205,92,152,228,99,212,212,209,94,215,218,95,206,179,236,219,204,102,196,110,160,218,140,228,102,218,177,172,193,95,209,215,203,220,78,213,191,189,168,204,218,192,89,213,223,208,230,168,202,169,222,56,155,203,73,197,156,208,222,127,212,223,160,217,201,223,205,217,100,221,208,143,218,22,208,224,86,209,90,220,144,182,139,219,79,194,183,225,216,209,190,102,221,220,203,152,215,113,230,106,212,221,106,197,111,185,211,83,162,200,208,219,124,210,92,161,216,221,79,208,166,211,142,179,222,197,79,185,226,182,193,195,170,202,113,192,231,228,197,180,152,223,155,215,97,213,85,205,145,200,89,209,201,139,217,163,133,206,174,112,205,213,217,123,201,136,132,221,206,105,200,139,183,160,215,134,221,208,181,208,101,210,225,116,183,217,80,186,120,162,194,201,97,188,209,174,122,229,214,205,120,218,228,228,83,134,201,119,171,210,55,227,107,225,64,174,203,130,213,226,95,206,157,139,154,171,139,224,219,154,190,206,218,217,221,86,135,217,213,183,118,187,186,128,212,79,184,186,156,188,134,216,172,155,195,210,176,223,125,128,217,213,214,217,94,189,192,133,167,85,187,217,60,158,221,122,217,117,224,95,206,89,199,74,205,210,70,209,83,196,77,169,188,228,114,188,105,229,182,174,223,59,181,205,127,225,222,128,190,219,112,197,115,222,205,115,184,214,210,238,196,212,85,207,86,183,215,178,187,112,194,176,212,90,217,80,164,179,187,138,221,214,203,96,227,198,82,209,207,96,208,76,228,224,205,207,186,178,221,224,189,143,229,182,229,132,213,201,186,218,67,222,221,83,216,44,220,68,214,103,137,210,48,210,211,88,222,169,225,155,181,200,207,62,219,152,179,130,215,86,183,90,202,164,179,205,128,219,224,148,190,117,213,221,184,175,146,223,216,82,230,92,201,231,218,172,207,209,189,150,216,142,226,46,197,219,92,228,168,216,111,221,204,216,190,180,189,206,108,204,101,224,74,223,65,181,142,211,203,122,220,214,66,179,185,222,116,120,209,222,101,172,209,232,56,223,177,212,107,191,168,193,193,105,212,105,163,209,191,117,194,170,211,190,121,221,90,161,207,67,226,90,216,60,171,219,89,220,184,214,194,144,182,214,208,196,133,226,214,77,190,215,63,115,208,92,227,204,68,197,77,178,188,204,216,202,194,152,175,181,208,131,196,223,128,177,116,207,102,220,121,216,199,202,207,74,211,196,217,177,214,218,221,93,219,192,215,143,139,183,226,83,102,219,211,100,194,47,119,204,160,143,180,149,183,226,96,172,212,186,198,207,182,207,221,164,146,224,61,200,140,147,196,214,60,226,215,111,216,186,180,161,234,196,124,208,214,221,189,178,112,168,208,136,169,181,202,124,201,74,211,143,192,175,119,163,202,67,191,172,148,179,223,122,217,117,180,190,186,209,72,193,182,218,80,156,138,201,202,218,208,149,190,109,224,64,192,114,219,199,210,208,97,232,205,197,188,189,87,213,152,196,164,131,225,94,219,205,163,191,172,196,189,206,110,201,73,191,122,210,173,208,73,221,136,222,185,224,74,213,86,185,221,170,210,69,165,156,210,102,211,210,221,197,209,154,127,212,180,208,139,231,207,50,166,96,150,158,206,228,214,202,95,162,220,191,136,217,54,155,201,140,179,191,91,213,220,74,145,216,232,45,208,217,209,182,160,182,100,221,155,219,227,160,180,209,147,174,212,67,209,82,213,148,208,51,176,88,210,71,117,174,218,82,211,188,170,210,186,136,176,220,157,189,167,190,212,160,212,135,201,219,49,162,165,209,117,175,213,152,176,220,221,124,150,204,220,73,228,194,218,57,195,173,159,173,175,206,176,229,79,164,201,203,152,214,116,137,219,66,222,214,80,175,202,90,225,113,219,206,78,190,89,214,50,209,154,188,227,194,157,195,74,186,206,130,198,73,212,60,204,122,222,99,205,196,229,213,83,230,108,171,126,192,104,216,207,117,217,197,214,79,207,110,221,79,217,144,206,160,206,172,197,183,207,217,207,113,210,221,71,161,221,164,227,214,142,177,185,180,103,130,198,123,205,74,216,102,219,160,217,75,204,114,192,213,166,188,118,222,227,92,195,219,161,200,221,69,203,143,198,198,217,198,66,212,50,208,116,199,125,210,207,167,225,116,207,97,184,99,220,184,203,184,219,177,167,202,214,55,207,161,197,122,212,226,187,96,216,201,188,135,224,207,139,225,230,220,121,221,107,212,66,170,169,210,199,102,220,94,159,184,207,92,207,231,214,125,227,220,205,58,193,203,215,223,229,78,196,170,185,196,162,234,56,201,123,171,231,196,86,162,199,213,220,68,200,68,205,88,225,135,220,82,182,215,222,79,152,230,62,162,218,184,224,67,206,99,189,124,214,197,73,204,105,221,179,102,218,232,80,214,181,170,204,165,216,207,217,212,195,176,215,106,192,160,221,182,217,57,211,88,198,233,113,171,204,138,193,209,225,59,176,184,134,223,151,193,200,217,100,225,79,180,142,190,123,222,80,232,216,133,216,148,211,110,198,96,187,224,95,208,112,178,227,94,171,96,181,209,170,225,196,206,94,216,87,217,171,191,82,218,127,227,176,219,207,230,79,214,203,105,213,143,174,188,125,193,220,60,215,172,214,101,211,110,161,117,187,180,125,218,220,62,208,203,217,87,198,156,216,226,161,161,223,224,72,178,198,213,195,219,208,140,175,217,74,201,201,66,186,154,229,89,226,169,204,87,184,85,161,133,201,80,176,188,114,224,77,207,126,202,83,219,200,125,172,169,190,216,80,88,221,68,218,133,216,117,217,157,217,170,190,124,214,210,156,231,84,207,204,113,200,70,222,162,208,227,92,223,136,167,195,221,221,77,173,213,109,214,117,211,217,89,217,91,210,152,194,206,202,110,216,177,190,207,227,185,172,230,172,207,171,199,234,207,149,194,192,179,212,209,210,101,198,225,85,164,211,110,194,182,211,224,65,228,218,79,224,81,122,208,154,129,206,92,193,171,148,188,221,80,220,161,165,166,161,214,99,210,64,174,224,221,105,200,122,230,216,94,223,128,225,161,219,126,187,137,191,222,214,148,151,198,218,210,110,208,228,184,211,35,202,218,195,216,115,212,95,177,199,101,184,208,202,212,134,193,129,192,81,182,223,70,226,230,134,167,183,198,222,227,227,226,63,213,109,187,177,219,223,203,144,179,209,103,177,181,158,221,90,222,166,207,175,230,207,99,205,234,210,210,168,223,143,210,187,209,204,150,209,213,208,193,221,214,77,215,199,81,197,82,177,190,210,231,79,179,221,64,182,199,82,204,204,95,172,187,178,209,86,222,220,118,192,223,88,220,77,174,104,224,137,182,186,96,207,198,74,152,196,217,206,79,214,208,204,180,94,215,81,177,160,201,164,173,205,76,199,220,228,91,215,155,226,79,133,181,136,182,226,96,221,109,209,223,71,202,95,217,87,202,204,183,210,187,212,81,226,184,224,88,170,214,198,226,142,212,81,209,189,172,192,221,216,123,221,126,204,218,222,76,205,73,225,221,73,204,108,201,88,174,197,136,223,90,189,56,207,147,206,212,73,201,83,204,112,137,227,67,208,137,219,225,65,200,186,99,214,97,215,74,203,65,199,216,108,216,80,206,219,104,226,180,225,199,186,197,226,157,102,177,107,231,156,141,226,70,220,216,223,64,214,66,201,174,170,207,46,202,131,173,218,125,217,157,234,192,159,174,209,95,196,224,59,220,69,211,130,203,222,88,208,86,198,127,219,228,75,218,170,168,198,128,215,54,211,167,186,117,211,162,221,219,105,223,99,223,127,202,218,213,143,194,181,200,180,230,224,97,181,132,173,202,221,57,151,220,77,220,160,206,188,101,197,72,213,95,193,212,189,105,226,100,205,201,56,211,93,178,212,88,208,83,213,165,219,183,236,121,220,210,94,212,171,186,218,137,212,129,175,203,223,134,194,95,193,191,105,229,208,102,196,120,191,221,217,65,206,200,74,168,180,199,217,119,223,68,211,125,204,105,180,164,215,227,128,211,166,218,86,185,74,214,57,200,171,111,185,73,199,220,213,192,216,107,211,115,219,227,192,221,101,203,65,211,51,216,84,193,121,214,86,195,115,179,229,90,215,92,207,63,179,212,38,202,104,182,125,179,99,147,184,210,166,227,232,164,120,218,169,203,154,192,224,217,122,160,205,206,221,80,191,217,166,202,78,206,147,202,155,195,76,204,136,191,112,195,160,147,226,91,224,216,212,177,188,165,174,130,203,221,220,133,209,147,216,69,159,155,143,213,94,227,139,209,163,183,199,112,217,213,98,217,96,185,158,173,229,51,209,195,227,214,161,213,83,168,229,209,118,221,224,59,179,161,220,209,193,199,199,212,107,226,219,204,117,166,223,122,166,181,163,176,223,176,130,223,221,202,89,188,147,160,143,218,223,206,151,201,161,130,176,175,138,126,209,112,230,94,211,17,103,218,73,218,131,210,104,214,63,222,38,135,140,215,143,215,191,185,223,207,215,203,46,219,207,93,177,85,213,191,223,56,181,209,82,210,221,66,210,195,223,184,138,217,48,194,73,150,199,220,183,209,60,194,103,218,103,211,216,124,197,217,185,106,185,207,174,165,204,138,220,68,218,151,202,68,214,155,183,221,66,216,61,218,122,214,178,202,178,217,142,215,126,187,148,219,98,180,222,217,80,210,203,43,208,154,220,101,167,206,211,212,208,72,147,225,139,174,207,36,200,234,205,211,180,205,202,126,159,186,116,211,154,192,155,194,168,198,160,218,220,202,153,222,215,66,174,128,211,104,136,171,235,219,112,156,209,109,203,132,192,181,215,112,205,68,215,82,213,117,189,221,186,211,171,208,136]
+[178,205,218,148,184,163,221,185,200,228,172,155,210,222,88,206,226,67,132,212,91,206,104,212,174,205,132,159,230,175,216,198,227,190,212,198,122,213,169,204,92,197,118,191,191,224,69,219,197,72,218,77,175,111,155,217,220,170,231,91,221,217,95,146,177,123,195,205,151,209,207,36,202,200,226,176,232,53,167,199,89,184,213,104,154,153,216,214,215,174,205,72,211,78,221,212,232,223,73,158,220,158,202,222,189,165,205,175,222,132,126,179,219,110,209,158,208,98,176,192,226,34,158,205,126,178,224,182,227,100,152,191,169,195,163,172,208,117,199,217,167,217,157,163,194,217,200,217,23,221,209,146,150,204,200,125,215,232,68,147,212,41,223,178,152,173,210,139,198,182,196,207,95,176,205,223,83,216,85,207,210,52,177,178,230,197,119,226,99,182,210,212,77,138,199,123,179,111,219,69,223,65,204,215,83,197,87,211,132,216,135,178,157,166,216,85,170,195,208,190,175,134,220,200,67,221,211,66,227,222,226,190,209,205,67,207,139,208,127,186,205,168,221,179,223,117,148,221,216,80,189,125,199,202,64,218,77,195,190,221,181,98,143,214,220,97,187,127,219,122,216,87,138,212,194,112,219,227,101,220,100,164,234,109,221,102,223,89,184,205,219,77,188,223,172,171,175,152,175,137,213,197,114,205,221,138,181,174,227,73,147,144,178,147,215,152,182,204,80,210,123,211,121,209,224,224,219,211,163,133,187,148,151,163,221,94,133,213,72,187,224,216,162,154,224,184,118,204,220,154,117,220,162,202,223,195,110,197,151,224,88,182,217,221,214,118,218,118,164,205,97,221,183,154,206,197,74,170,219,103,230,215,192,224,78,184,72,201,227,221,191,181,104,190,224,221,99,123,206,102,228,202,74,195,96,225,176,232,231,96,225,206,61,173,101,190,211,90,213,199,203,184,209,173,160,207,203,99,201,195,132,195,214,148,211,227,192,215,212,127,162,213,114,178,111,207,71,129,182,212,75,176,209,137,213,224,220,70,232,116,228,179,200,184,222,93,202,71,219,123,213,119,204,173,135,118,207,96,216,107,210,202,189,172,211,76,146,195,169,83,227,62,219,223,125,158,202,226,98,214,225,76,156,211,204,110,190,224,200,64,228,223,72,213,102,214,178,219,128,211,127,187,213,160,216,134,202,150,186,201,195,230,199,206,193,205,219,216,84,213,223,192,222,109,187,209,79,153,204,224,72,167,190,183,183,228,133,227,103,197,225,203,205,201,207,213,210,78,138,195,133,195,180,190,185,207,189,100,191,229,198,129,226,135,205,164,226,190,73,200,231,209,216,147,221,228,165,213,209,181,157,222,117,216,58,148,219,92,222,219,63,191,218,186,197,158,172,204,109,204,102,223,229,91,192,217,186,204,144,173,209,95,186,222,225,178,183,222,71,193,182,232,63,227,228,82,209,162,219,183,222,122,212,195,60,219,130,223,69,216,111,198,224,80,223,74,164,148,227,193,216,139,207,198,148,227,199,169,230,225,86,138,165,206,91,213,164,215,229,173,216,210,92,166,205,102,196,78,202,74,187,202,229,191,222,57,206,115,183,94,225,173,198,109,212,224,126,215,44,221,80,196,95,177,218,217,198,109,218,191,221,214,227,71,207,123,206,90,219,218,108,181,214,123,150,204,214,208,225,199,191,192,213,93,217,149,178,199,106,204,80,208,60,131,132,232,218,213,115,202,94,175,227,217,114,200,222,178,180,185,192,193,205,173,226,217,222,197,184,205,211,136,214,221,62,213,89,169,184,135,198,98,216,225,71,230,133,213,154,216,189,122,224,108,204,88,207,227,93,191,204,214,66,153,219,209,203,218,237,229,222,148,174,218,107,214,169,223,216,173,220,148,199,188,175,203,142,188,210,98,167,174,224,103,156,97,220,111,216,70,130,221,216,115,166,164,162,210,216,69,173,203,77,216,175,172,210,98,199,213,88,221,82,184,131,201,220,133,211,116,156,219,213,227,128,221,237,172,217,64,180,221,70,169,220,216,134,215,177,190,197,229,221,226,74,189,190,219,86,222,74,213,106,184,182,205,221,229,143,201,198,162,209,128,221,209,60,216,146,228,106,215,154,214,202,82,194,141,168,217,213,80,140,114,220,149,229,215,150,211,170,199,109,218,108,206,125,204,164,183,148,193,153,188,143,160,199,210,124,225,105,178,75,222,112,205,75,199,83,222,215,158,104,205,70,215,223,158,220,99,124,194,91,219,65,209,149,234,188,222,218,200,66,208,101,208,188,201,207,198,160,205,219,118,170,195,141,128,182,189,211,51,179,216,112,152,196,224,80,223,177,218,127,186,113,151,117,159,184,200,231,212,134,222,68,216,204,90,151,202,158,173,146,144,209,222,89,171,188,206,215,117,191,223,80,215,68,151,159,210,74,208,105,204,105,174,228,67,147,202,214,41,222,163,183,142,227,188,200,208,132,157,191,195,94,166,201,155,214,132,185,195,165,214,219,156,217,100,147,220,105,220,223,225,117,176,166,207,134,218,202,172,183,217,76,229,139,217,87,208,181,214,169,154,199,231,208,200,157,213,222,49,207,191,139,219,218,82,122,154,137,190,176,192,180,231,80,208,136,185,112,189,187,192,208,219,110,208,102,186,225,220,218,235,214,96,133,198,199,95,152,83,219,190,124,148,222,213,207,145,217,168,200,32,208,198,171,205,218,104,218,136,230,77,139,178,212,214,129,211,233,93,221,95,155,153,221,151,201,108,193,216,172,208,72,177,217,208,225,60,81,172,234,180,146,184,180,199,222,224,182,106,183,105,221,208,81,221,137,211,44,198,224,205,82,187,164,147,213,163,229,192,219,218,190,228,221,218,116,231,97,182,126,213,193,173,207,103,207,122,161,156,193,153,192,92,198,155,223,224,106,223,170,177,225,40,206,203,221,152,208,116,203,105,197,221,65,226,111,224,215,215,142,154,211,84,184,135,169,212,213,76,229,225,130,207,85,209,177,116,140,150,229,209,105,164,186,214,219,62,148,203,230,118,207,216,217,211,114,201,134,217,87,197,124,209,186,184,164,189,163,212,85,219,106,209,220,168,221,212,112,189,183,229,200,192,106,224,114,220,95,141,223,222,219,226,79,187,202,212,222,193,128,223,209,160,178,197,222,111,152,114,164,206,179,189,183,122,212,149,150,130,214,115,195,118,204,208,120,195,163,159,220,125,222,174,211,144,140,220,80,173,204,87,215,149,227,97,229,221,119,213,139,212,82,215,68,213,136,186,168,202,85,213,93,168,218,63,222,121,213,90,218,94,197,221,182,220,112,224,221,83,215,194,138,181,197,221,91,186,194,99,179,213,210,76,188,214,216,79,213,92,220,94,201,108,213,131,221,153,219,74,159,172,218,217,219,108,209,35,217,112,157,206,148,211,126,214,107,191,105,164,224,220,126,204,201,211,119,203,129,165,205,130,218,137,211,178,204,215,206,119,209,75,193,182,115,178,207,217,220,184,199,167,189,230,140,208,167,173,195,136,220,205,64,186,159,194,75,213,189,110,189,208,120,176,182,162,196,183,137,134,198,172,193,184,172,212,189,214,200,95,167,199,110,195,116,180,204,66,217,126,222,226,193,166,143,121,218,224,196,140,226,142,206,66,214,140,218,144,201,221,152,204,154,221,101,225,69,216,212,128,181,200,180,206,136,193,226,226,219,141,168,202,209,105,178,128,208,197,145,163,218,124,220,147,220,156,211,69,212,226,132,223,165,211,110,229,194,193,204,208,90,218,106,152,215,134,180,182,204,211,80,211,108,204,102,208,102,192,94,191,206,203,140,165,213,197,105,186,186,210,62,208,165,213,174,186,115,211,164,193,128,201,169,217,167,217,174,222,167,129,170,217,218,182,159,113,208,85,219,126,219,89,172,213,212,69,205,135,124,205,67,208,52,186,160,225,125,190,65,227,168,180,166,151,194,205,88,138,204,110,124,223,80,193,106,179,183,163,220,148,205,218,200,221,198,202,190,213,87,227,239,81,213,115,211,219,201,204,105,216,83,210,95,170,219,197,86,228,86,209,125,231,83,198,194,171,144,183,222,216,216,226,204,109,216,115,180,227,116,207,223,84,190,215,117,212,79,214,230,218,134,208,92,209,99,181,220,61,212,85,199,185,174,171,220,221,57,146,224,214,81,183,218,152,163,193,72,203,111,130,222,204,217,229,209,201,189,220,213,72,192,214,221,157,197,124,209,100,223,162,223,73,85,221,68,210,46,205,76,118,160,180,200,85,218,34,196,170,204,88,168,224,71,188,207,166,181,210,101,148,215,76,219,217,104,199,180,210,208,69,201,202,216,224,89,206,88,128,125,196,203,106,183,182,84,228,200,120,171,166,153,215,210,82,210,65,163,131,213,83,180,205,92,152,228,99,212,212,209,94,215,218,95,206,179,236,219,204,102,196,110,160,218,140,228,102,218,177,172,193,95,209,215,203,220,78,213,191,189,168,204,218,192,89,213,223,208,230,168,202,169,222,56,155,203,73,197,156,208,222,127,212,223,160,217,201,223,205,217,100,221,208,143,218,22,208,224,86,209,90,220,144,182,139,219,79,194,183,225,216,209,190,102,221,220,203,152,215,113,230,106,212,221,106,197,111,185,211,83,162,200,208,219,124,210,92,161,216,221,79,208,166,211,142,179,222,197,79,185,226,182,193,195,170,202,113,192,231,228,197,180,152,223,155,215,97,213,85,205,145,200,89,209,201,139,217,163,133,206,174,112,205,213,217,123,201,136,132,221,206,105,200,139,183,160,215,134,221,208,181,208,101,210,225,116,183,217,80,186,120,162,194,201,97,188,209,174,122,229,214,205,120,218,228,228,83,134,201,119,171,210,55,227,107,225,64,174,203,130,213,226,95,206,157,139,154,171,139,224,219,154,190,206,218,217,221,86,135,217,213,183,118,187,186,128,212,79,184,186,156,188,134,216,172,155,195,210,176,223,125,128,217,213,214,217,94,189,192,133,167,85,187,217,60,158,221,122,217,117,224,95,206,89,199,74,205,210,70,209,83,196,77,169,188,228,114,188,105,229,182,174,223,59,181,205,127,225,222,128,190,219,112,197,115,222,205,115,184,214,210,238,196,212,85,207,86,183,215,178,187,112,194,176,212,90,217,80,164,179,187,138,221,214,203,96,227,198,82,209,207,96,208,76,228,224,205,207,186,178,221,224,189,143,229,182,229,132,213,201,186,218,67,222,221,83,216,44,220,68,214,103,137,210,48,210,211,88,222,169,225,155,181,200,207,62,219,152,179,130,215,86,183,90,202,164,179,205,128,219,224,148,190,117,213,221,184,175,146,223,216,82,230,92,201,231,218,172,207,209,189,150,216,142,226,46,197,219,92,228,168,216,111,221,204,216,190,180,189,206,108,204,101,224,74,223,65,181,142,211,203,122,220,214,66,179,185,222,116,120,209,222,101,172,209,232,56,223,177,212,107,191,168,193,193,105,212,105,163,209,191,117,194,170,211,190,121,221,90,161,207,67,226,90,216,60,171,219,89,220,184,214,194,144,182,214,208,196,133,226,214,77,190,215,63,115,208,92,227,204,68,197,77,178,188,204,216,202,194,152,175,181,208,131,196,223,128,177,116,207,102,220,121,216,199,202,207,74,211,196,217,177,214,218,221,93,219,192,215,143,139,183,226,83,102,219,211,100,194,47,119,204,160,143,180,149,183,226,96,172,212,186,198,207,182,207,221,164,146,224,61,200,140,147,196,214,60,226,215,111,216,186,180,161,234,196,124,208,214,221,189,178,112,168,208,136,169,181,202,124,201,74,211,143,192,175,119,163,202,67,191,172,148,179,223,122,217,117,180,190,186,209,72,193,182,218,80,156,138,201,202,218,208,149,190,109,224,64,192,114,219,199,210,208,97,232,205,197,188,189,87,213,152,196,164,131,225,94,219,205,163,191,172,196,189,206,110,201,73,191,122,210,173,208,73,221,136,222,185,224,74,213,86,185,221,170,210,69,165,156,210,102,211,210,221,197,209,154,127,212,180,208,139,231,207,50,166,96,150,158,206,228,214,202,95,162,220,191,136,217,54,155,201,140,179,191,91,213,220,74,145,216,232,45,208,217,209,182,160,182,100,221,155,219,227,160,180,209,147,174,212,67,209,82,213,148,208,51,176,88,210,71,117,174,218,82,211,188,170,210,186,136,176,220,157,189,167,190,212,160,212,135,201,219,49,162,165,209,117,175,213,152,176,220,221,124,150,204,220,73,228,194,218,57,195,173,159,173,175,206,176,229,79,164,201,203,152,214,116,137,219,66,222,214,80,175,202,90,225,113,219,206,78,190,89,214,50,209,154,188,227,194,157,195,74,186,206,130,198,73,212,60,204,122,222,99,205,196,229,213,83,230,108,171,126,192,104,216,207,117,217,197,214,79,207,110,221,79,217,144,206,160,206,172,197,183,207,217,207,113,210,221,71,161,221,164,227,214,142,177,185,180,103,130,198,123,205,74,216,102,219,160,217,75,204,114,192,213,166,188,118,222,227,92,195,219,161,200,221,69,203,143,198,198,217,198,66,212,50,208,116,199,125,210,207,167,225,116,207,97,184,99,220,184,203,184,219,177,167,202,214,55,207,161,197,122,212,226,187,96,216,201,188,135,224,207,139,225,230,220,121,221,107,212,66,170,169,210,199,102,220,94,159,184,207,92,207,231,214,125,227,220,205,58,193,203,215,223,229,78,196,170,185,196,162,234,56,201,123,171,231,196,86,162,199,213,220,68,200,68,205,88,225,135,220,82,182,215,222,79,152,230,62,162,218,184,224,67,206,99,189,124,214,197,73,204,105,221,179,102,218,232,80,214,181,170,204,165,216,207,217,212,195,176,215,106,192,160,221,182,217,57,211,88,198,233,113,171,204,138,193,209,225,59,176,184,134,223,151,193,200,217,100,225,79,180,142,190,123,222,80,232,216,133,216,148,211,110,198,96,187,224,95,208,112,178,227,94,171,96,181,209,170,225,196,206,94,216,87,217,171,191,82,218,127,227,176,219,207,230,79,214,203,105,213,143,174,188,125,193,220,60,215,172,214,101,211,110,161,117,187,180,125,218,220,62,208,203,217,87,198,156,216,226,161,161,223,224,72,178,198,213,195,219,208,140,175,217,74,201,201,66,186,154,229,89,226,169,204,87,184,85,161,133,201,80,176,188,114,224,77,207,126,202,83,219,200,125,172,169,190,216,80,88,221,68,218,133,216,117,217,157,217,170,190,124,214,210,156,231,84,207,204,113,200,70,222,162,208,227,92,223,136,167,195,221,221,77,173,213,109,214,117,211,217,89,217,91,210,152,194,206,202,110,216,177,190,207,227,185,172,230,172,207,171,199,234,207,149,194,192,179,212,209,210,101,198,225,85,164,211,110,194,182,211,224,65,228,218,79,224,81,122,208,154,129,206,92,193,171,148,188,221,80,220,161,165,166,161,214,99,210,64,174,224,221,105,200,122,230,216,94,223,128,225,161,219,126,187,137,191,222,214,148,151,198,218,210,110,208,228,184,211,35,202,218,195,216,115,212,95,177,199,101,184,208,202,212,134,193,129,192,81,182,223,70,226,230,134,167,183,198,222,227,227,226,63,213,109,187,177,219,223,203,144,179,209,103,177,181,158,221,90,222,166,207,175,230,207,99,205,234,210,210,168,223,143,210,187,209,204,150,209,213,208,193,221,214,77,215,199,81,197,82,177,190,210,231,79,179,221,64,182,199,82,204,204,95,172,187,178,209,86,222,220,118,192,223,88,220,77,174,104,224,137,182,186,96,207,198,74,152,196,217,206,79,214,208,204,180,94,215,81,177,160,201,164,173,205,76,199,220,228,91,215,155,226,79,133,181,136,182,226,96,221,109,209,223,71,202,95,217,87,202,204,183,210,187,212,81,226,184,224,88,170,214,198,226,142,212,81,209,189,172,192,221,216,123,221,126,204,218,222,76,205,73,225,221,73,204,108,201,88,174,197,136,223,90,189,56,207,147,206,212,73,201,83,204,112,137,227,67,208,137,219,225,65,200,186,99,214,97,215,74,203,65,199,216,108,216,80,206,219,104,226,180,225,199,186,197,226,157,102,177,107,231,156,141,226,70,220,216,223,64,214,66,201,174,170,207,46,202,131,173,218,125,217,157,234,192,159,174,209,95,196,224,59,220,69,211,130,203,222,88,208,86,198,127,219,228,75,218,170,168,198,128,215,54,211,167,186,117,211,162,221,219,105,223,99,223,127,202,218,213,143,194,181,200,180,230,224,97,181,132,173,202,221,57,151,220,77,220,160,206,188,101,197,72,213,95,193,212,189,105,226,100,205,201,56,211,93,178,212,88,208,83,213,165,219,183,236,121,220,210,94,212,171,186,218,137,212,129,175,203,223,134,194,95,193,191,105,229,208,102,196,120,191,221,217,65,206,200,74,168,180,199,217,119,223,68,211,125,204,105,180,164,215,227,128,211,166,218,86,185,74,214,57,200,171,111,185,73,199,220,213,192,216,107,211,115,219,227,192,221,101,203,65,211,51,216,84,193,121,214,86,195,115,179,229,90,215,92,207,63,179,212,38,202,104,182,125,179,99,147,184,210,166,227,232,164,120,218,169,203,154,192,224,217,122,160,205,206,221,80,191,217,166,202,78,206,147,202,155,195,76,204,136,191,112,195,160,147,226,91,224,216,212,177,188,165,174,130,203,221,220,133,209,147,216,69,159,155,143,213,94,227,139,209,163,183,199,112,217,213,98,217,96,185,158,173,229,51,209,195,227,214,161,213,83,168,229,209,118,221,224,59,179,161,220,209,193,199,199,212,107,226,219,204,117,166,223,122,166,181,163,176,223,176,130,223,221,202,89,188,147,160,143,218,223,206,151,201,161,130,176,175,138,126,209,112,230,94,211,17,103,218,73,218,131,210,104,214,63,222,38,135,140,215,143,215,191,185,223,207,215,203,46,219,207,93,177,85,213,191,223,56,181,209,82,210,221,66,210,195,223,184,138,217,48,194,73,150,199,220,183,209,60,194,103,218,103,211,216,124,197,217,185,106,185,207,174,165,204,138,220,68,218,151,202,68,214,155,183,221,66,216,61,218,122,214,178,202,178,217,142,215,126,187,148,219,98,180,222,217,80,210,203,43,208,154,220,101,167,206,211,212,208,72,147,225,139,174,207,36,200,234,205,211,180,205,202,126,159,186,116,211,154,192,155,194,168,198,160,218,220,202,153,222,215,66,174,128,211,104,136,171,235,219,112,156,209,109,203,132,192,181,215,112,205,68,215,82,213,117,189,221,186,211,171,208,136,189,128]

ivf.pid.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ebd54f5303b992de51affa85afdcf6dd306dcb10513567891fe71259689ee281
-size 1877208
+oid sha256:e59d987d0d5f148eb6fb4b5d3ea6894a45a382a4bb55a7b669dba0b4bc60cd96
+size 1876824

metadata.json

@@ -37,7 +37,7 @@
     "checkpoint":"colbert-ir/colbertv2.0",
     "triples":"/future/u/okhattab/root/unit/experiments/2021.10/downstream.distillation.round2.2_score/round2.nway6.cosine.ib/examples.64.json",
     "collection":[
-      "list with 4152 elements starting with...",
+      "list with 4154 elements starting with...",
       [
         "Deep neural networks have demonstrated remarkable performance in supervised learning tasks but require large amounts of labeled data. Self-supervised learning offers an alternative paradigm, enabling the model to learn from data without explicit labels. Information theory has been instrumental in understanding and optimizing deep neural networks. Specifically, the information bottleneck principle has been applied to optimize the trade-off between compression and relevant information preservation in supervised settings. However, the optimal information objective in self-supervised learning remains unclear. In this paper, we review various approaches to self-supervised learning from an information-theoretic standpoint and present a unified framework that formalizes the self-supervised information-theoretic learning problem. We integrate existing research into a coherent framework, examine recent self-supervised methods, and identify research opportunities and challenges. Moreover, we discuss empirical measurement of information-theoretic quantities and their estimators. This paper offers a comprehensive review of the intersection between information theory, self-supervised learning, and deep neural networks.",
         "Pre-trained large language models (LLMs) capture procedural knowledge about the world. Recent work has leveraged LLM's ability to generate abstract plans to simplify challenging control tasks, either by action scoring, or action modeling (fine-tuning). However, the transformer architecture inherits several constraints that make it difficult for the LLM to directly serve as the agent: e.g. limited input lengths, fine-tuning inefficiency, bias from pre-training, and incompatibility with non-text environments. To maintain compatibility with a low-level trainable actor, we propose to instead use the knowledge in LLMs to simplify the control problem, rather than solving it. We propose the Plan, Eliminate, and Track (PET) framework. The Plan module translates a task description into a list of high-level sub-tasks. The Eliminate module masks out irrelevant objects and receptacles from the observation for the current sub-task. Finally, the Track module determines whether the agent has accomplished each sub-task. On the AlfWorld instruction following benchmark, the PET framework leads to a significant 15% improvement over SOTA for generalization to human goal specifications.",
@@ -50,7 +50,7 @@
     "root":".ragatouille/",
     "experiment":"colbert",
     "index_root":null,
-    "name":"2024-07/03/06.53.57",
+    "name":"2024-07/03/08.55.07",
     "rank":0,
     "nranks":1,
     "amp":true,
@@ -59,8 +59,8 @@
   },
   "num_chunks":1,
   "num_partitions":8192,
-  "num_embeddings":712645,
-  "avg_doclen":171.6389691715,
+  "num_embeddings":712962,
+  "avg_doclen":171.6326432354,
   "RAGatouille":{
     "index_config":{
       "index_type":"PLAID",

pid_docid_map.json

@@ -4150,5 +4150,7 @@
   "4148":"2407.02490",
   "4149":"2407.02490",
   "4150":"2407.01489",
-  "4151":"2407.01489"
+  "4151":"2407.01489",
+  "4152":"2407.01370",
+  "4153":"2407.01370"
 }

plan.json

@@ -37,7 +37,7 @@
         "checkpoint": "colbert-ir\/colbertv2.0",
         "triples": "\/future\/u\/okhattab\/root\/unit\/experiments\/2021.10\/downstream.distillation.round2.2_score\/round2.nway6.cosine.ib\/examples.64.json",
         "collection": [
-            "list with 4152 elements starting with...",
+            "list with 4154 elements starting with...",
             [
                 "Deep neural networks have demonstrated remarkable performance in supervised learning tasks but require large amounts of labeled data. Self-supervised learning offers an alternative paradigm, enabling the model to learn from data without explicit labels. Information theory has been instrumental in understanding and optimizing deep neural networks. Specifically, the information bottleneck principle has been applied to optimize the trade-off between compression and relevant information preservation in supervised settings. However, the optimal information objective in self-supervised learning remains unclear. In this paper, we review various approaches to self-supervised learning from an information-theoretic standpoint and present a unified framework that formalizes the self-supervised information-theoretic learning problem. We integrate existing research into a coherent framework, examine recent self-supervised methods, and identify research opportunities and challenges. Moreover, we discuss empirical measurement of information-theoretic quantities and their estimators. This paper offers a comprehensive review of the intersection between information theory, self-supervised learning, and deep neural networks.",
                 "Pre-trained large language models (LLMs) capture procedural knowledge about the world. Recent work has leveraged LLM's ability to generate abstract plans to simplify challenging control tasks, either by action scoring, or action modeling (fine-tuning). However, the transformer architecture inherits several constraints that make it difficult for the LLM to directly serve as the agent: e.g. limited input lengths, fine-tuning inefficiency, bias from pre-training, and incompatibility with non-text environments. To maintain compatibility with a low-level trainable actor, we propose to instead use the knowledge in LLMs to simplify the control problem, rather than solving it. We propose the Plan, Eliminate, and Track (PET) framework. The Plan module translates a task description into a list of high-level sub-tasks. The Eliminate module masks out irrelevant objects and receptacles from the observation for the current sub-task. Finally, the Track module determines whether the agent has accomplished each sub-task. On the AlfWorld instruction following benchmark, the PET framework leads to a significant 15% improvement over SOTA for generalization to human goal specifications.",
@@ -50,7 +50,7 @@
         "root": ".ragatouille\/",
         "experiment": "colbert",
         "index_root": null,
-        "name": "2024-07\/03\/06.53.57",
+        "name": "2024-07\/03\/08.55.07",
         "rank": 0,
         "nranks": 1,
         "amp": true,
@@ -59,6 +59,6 @@
     },
     "num_chunks": 1,
     "num_partitions": 8192,
-    "num_embeddings_est": 712644.9693603516,
-    "avg_doclen_est": 171.6389617919922
+    "num_embeddings_est": 712962.0058898926,
+    "avg_doclen_est": 171.6326446533203
 }