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Self-Rewarding Language Models
Paper • 2401.10020 • Published • 140 -
Orion-14B: Open-source Multilingual Large Language Models
Paper • 2401.12246 • Published • 10 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 48 -
MM-LLMs: Recent Advances in MultiModal Large Language Models
Paper • 2401.13601 • Published • 44
Collections
Discover the best community collections!
Collections including paper arxiv:2407.01920
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Eliminating Position Bias of Language Models: A Mechanistic Approach
Paper • 2407.01100 • Published • 6 -
To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models
Paper • 2407.01920 • Published • 13 -
LiteSearch: Efficacious Tree Search for LLM
Paper • 2407.00320 • Published • 37
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Learn Your Reference Model for Real Good Alignment
Paper • 2404.09656 • Published • 82 -
Aligning Teacher with Student Preferences for Tailored Training Data Generation
Paper • 2406.19227 • Published • 24 -
Self-Play Preference Optimization for Language Model Alignment
Paper • 2405.00675 • Published • 22 -
CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues
Paper • 2404.03820 • Published • 23
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How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 29 -
From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries
Paper • 2406.12824 • Published • 20 -
Tokenization Falling Short: The Curse of Tokenization
Paper • 2406.11687 • Published • 14 -
Iterative Length-Regularized Direct Preference Optimization: A Case Study on Improving 7B Language Models to GPT-4 Level
Paper • 2406.11817 • Published • 13
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Large Language Model Unlearning via Embedding-Corrupted Prompts
Paper • 2406.07933 • Published • 6 -
Block Transformer: Global-to-Local Language Modeling for Fast Inference
Paper • 2406.02657 • Published • 36 -
Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoning
Paper • 2406.12050 • Published • 10 -
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 29
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Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
Paper • 2404.02575 • Published • 47 -
Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
Paper • 2404.12253 • Published • 52 -
SnapKV: LLM Knows What You are Looking for Before Generation
Paper • 2404.14469 • Published • 23 -
FlowMind: Automatic Workflow Generation with LLMs
Paper • 2404.13050 • Published • 32