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How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 30 -
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 • 15 -
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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Collections including paper arxiv:2406.11817
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A Critical Evaluation of AI Feedback for Aligning Large Language Models
Paper • 2402.12366 • Published • 3 -
PERL: Parameter Efficient Reinforcement Learning from Human Feedback
Paper • 2403.10704 • Published • 57 -
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Paper • 2403.03507 • Published • 182 -
Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation
Paper • 2401.08417 • Published • 33
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Suppressing Pink Elephants with Direct Principle Feedback
Paper • 2402.07896 • Published • 9 -
Policy Improvement using Language Feedback Models
Paper • 2402.07876 • Published • 5 -
Direct Language Model Alignment from Online AI Feedback
Paper • 2402.04792 • Published • 29 -
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
Paper • 2401.01335 • Published • 64
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Self-Rewarding Language Models
Paper • 2401.10020 • Published • 144 -
Orion-14B: Open-source Multilingual Large Language Models
Paper • 2401.12246 • Published • 12 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 51 -
MM-LLMs: Recent Advances in MultiModal Large Language Models
Paper • 2401.13601 • Published • 45
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Trusted Source Alignment in Large Language Models
Paper • 2311.06697 • Published • 10 -
Diffusion Model Alignment Using Direct Preference Optimization
Paper • 2311.12908 • Published • 47 -
SuperHF: Supervised Iterative Learning from Human Feedback
Paper • 2310.16763 • Published • 1 -
Enhancing Diffusion Models with Text-Encoder Reinforcement Learning
Paper • 2311.15657 • Published • 2
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PockEngine: Sparse and Efficient Fine-tuning in a Pocket
Paper • 2310.17752 • Published • 12 -
S-LoRA: Serving Thousands of Concurrent LoRA Adapters
Paper • 2311.03285 • Published • 28 -
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
Paper • 2311.06243 • Published • 17 -
Fine-tuning Language Models for Factuality
Paper • 2311.08401 • Published • 28