Collection by 
18 days ago
Collections
Discover the best community collections!
Collections including paper arxiv:2402.00858
Collection by 
Aug 21, 2024
Collection by 
about 8 hours ago
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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 85 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 152 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
Collection by 
Feb 19, 2024
Collection by 
Feb 4, 2024
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OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 85 -
Efficient Exploration for LLMs
Paper • 2402.00396 • Published • 22 -
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
Transforming and Combining Rewards for Aligning Large Language Models
Paper • 2402.00742 • Published • 12
Collection by 
Mar 8, 2025
Collection by 
Feb 18, 2024
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Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 109 -
How to Train Data-Efficient LLMs
Paper • 2402.09668 • Published • 43 -
BitDelta: Your Fine-Tune May Only Be Worth One Bit
Paper • 2402.10193 • Published • 21 -
A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts
Paper • 2402.09727 • Published • 38
Collection by 
Dec 22, 2024
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In-Context Language Learning: Architectures and Algorithms
Paper • 2401.12973 • Published • 4 -
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
Transformers Can Achieve Length Generalization But Not Robustly
Paper • 2402.09371 • Published • 14 -
Emergence of Abstractions: Concept Encoding and Decoding Mechanism for In-Context Learning in Transformers
Paper • 2412.12276 • Published • 15
Collection by 
Sep 20, 2024
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Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 21 -
Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation
Paper • 2401.15688 • Published • 11 -
SliceGPT: Compress Large Language Models by Deleting Rows and Columns
Paper • 2401.15024 • Published • 73 -
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Paper • 2401.15071 • Published • 37
Collection by 
Aug 17, 2024
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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 21 -
ReFT: Reasoning with Reinforced Fine-Tuning
Paper • 2401.08967 • Published • 31 -
The Impact of Reasoning Step Length on Large Language Models
Paper • 2401.04925 • Published • 18
Collection by 18 days ago
Collection by Mar 8, 2025
Collection by Aug 21, 2024
Collection by Feb 18, 2024
-
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 109 -
How to Train Data-Efficient LLMs
Paper • 2402.09668 • Published • 43 -
BitDelta: Your Fine-Tune May Only Be Worth One Bit
Paper • 2402.10193 • Published • 21 -
A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts
Paper • 2402.09727 • Published • 38
Collection by about 8 hours ago
-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 85 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 152 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
Collection by Dec 22, 2024
-
In-Context Language Learning: Architectures and Algorithms
Paper • 2401.12973 • Published • 4 -
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
Transformers Can Achieve Length Generalization But Not Robustly
Paper • 2402.09371 • Published • 14 -
Emergence of Abstractions: Concept Encoding and Decoding Mechanism for In-Context Learning in Transformers
Paper • 2412.12276 • Published • 15
Collection by Feb 19, 2024
Collection by Sep 20, 2024
-
Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 21 -
Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation
Paper • 2401.15688 • Published • 11 -
SliceGPT: Compress Large Language Models by Deleting Rows and Columns
Paper • 2401.15024 • Published • 73 -
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Paper • 2401.15071 • Published • 37
Collection by Feb 4, 2024
-
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 85 -
Efficient Exploration for LLMs
Paper • 2402.00396 • Published • 22 -
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
Transforming and Combining Rewards for Aligning Large Language Models
Paper • 2402.00742 • Published • 12
Collection by Aug 17, 2024
-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 21 -
ReFT: Reasoning with Reinforced Fine-Tuning
Paper • 2401.08967 • Published • 31 -
The Impact of Reasoning Step Length on Large Language Models
Paper • 2401.04925 • Published • 18