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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 23 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 85 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
Collections
Discover the best community collections!
Collections including paper arxiv:2402.10200
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No More Adam: Learning Rate Scaling at Initialization is All You Need
Paper • 2412.11768 • Published • 43 -
TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks
Paper • 2412.14161 • Published • 51 -
HiRED: Attention-Guided Token Dropping for Efficient Inference of High-Resolution Vision-Language Models in Resource-Constrained Environments
Paper • 2408.10945 • Published • 11 -
PDFTriage: Question Answering over Long, Structured Documents
Paper • 2309.08872 • Published • 53
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The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs
Paper • 2210.14986 • Published • 5 -
Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2
Paper • 2311.10702 • Published • 20 -
Large Language Models as Optimizers
Paper • 2309.03409 • Published • 77 -
From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting
Paper • 2309.04269 • Published • 33
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Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 109 -
Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
Paper • 2404.12253 • Published • 55 -
Make Your LLM Fully Utilize the Context
Paper • 2404.16811 • Published • 55 -
ReFT: Representation Finetuning for Language Models
Paper • 2404.03592 • Published • 101
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Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 109 -
Large Language Models Cannot Self-Correct Reasoning Yet
Paper • 2310.01798 • Published • 36 -
Premise Order Matters in Reasoning with Large Language Models
Paper • 2402.08939 • Published • 28 -
Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
Paper • 2402.12875 • Published • 13
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Let's Verify Step by Step
Paper • 2305.20050 • Published • 11 -
LLM Critics Help Catch LLM Bugs
Paper • 2407.00215 • Published -
Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
Paper • 2407.21787 • Published • 13 -
Generative Verifiers: Reward Modeling as Next-Token Prediction
Paper • 2408.15240 • Published • 13
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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 23 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 85 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
-
The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs
Paper • 2210.14986 • Published • 5 -
Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2
Paper • 2311.10702 • Published • 20 -
Large Language Models as Optimizers
Paper • 2309.03409 • Published • 77 -
From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting
Paper • 2309.04269 • Published • 33
-
No More Adam: Learning Rate Scaling at Initialization is All You Need
Paper • 2412.11768 • Published • 43 -
TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks
Paper • 2412.14161 • Published • 51 -
HiRED: Attention-Guided Token Dropping for Efficient Inference of High-Resolution Vision-Language Models in Resource-Constrained Environments
Paper • 2408.10945 • Published • 11 -
PDFTriage: Question Answering over Long, Structured Documents
Paper • 2309.08872 • Published • 53
-
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 109 -
Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
Paper • 2404.12253 • Published • 55 -
Make Your LLM Fully Utilize the Context
Paper • 2404.16811 • Published • 55 -
ReFT: Representation Finetuning for Language Models
Paper • 2404.03592 • Published • 101
-
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 109 -
Large Language Models Cannot Self-Correct Reasoning Yet
Paper • 2310.01798 • Published • 36 -
Premise Order Matters in Reasoning with Large Language Models
Paper • 2402.08939 • Published • 28 -
Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
Paper • 2402.12875 • Published • 13
-
Let's Verify Step by Step
Paper • 2305.20050 • Published • 11 -
LLM Critics Help Catch LLM Bugs
Paper • 2407.00215 • Published -
Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
Paper • 2407.21787 • Published • 13 -
Generative Verifiers: Reward Modeling as Next-Token Prediction
Paper • 2408.15240 • Published • 13