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Collections including paper arxiv:2404.03715

Rho-1: Not All Tokens Are What You Need

Paper • 2404.07965 • Published Apr 11 • 79
VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time

Paper • 2404.10667 • Published Apr 16 • 12
Instruction-tuned Language Models are Better Knowledge Learners

Paper • 2402.12847 • Published Feb 20 • 25
DoRA: Weight-Decomposed Low-Rank Adaptation

Paper • 2402.09353 • Published Feb 14 • 18

Synthetic Data and Self-Improvement

Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18 • 135
Self-Discover: Large Language Models Self-Compose Reasoning Structures

Paper • 2402.03620 • Published Feb 6 • 102
OS-Copilot: Towards Generalist Computer Agents with Self-Improvement

Paper • 2402.07456 • Published Feb 12 • 39
Learning From Mistakes Makes LLM Better Reasoner

Paper • 2310.20689 • Published Oct 31, 2023 • 24

Papers-Fundamentals

RoFormer: Enhanced Transformer with Rotary Position Embedding

Paper • 2104.09864 • Published Apr 20, 2021 • 7
Attention Is All You Need

Paper • 1706.03762 • Published Jun 12, 2017 • 34
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 57

Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 57

Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 57

RecurrentGemma: Moving Past Transformers for Efficient Open Language Models

Paper • 2404.07839 • Published Apr 11 • 37
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 57

Papers - RLHF - Iterative Contrastive Self-Improvement

A batched on-policy algorithm that conducts self-improvement iteratively via contrastive learning

Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 57

Papers - Fine-tuning - RLHF - Direct Nash Optimization (DNO)

Reward expressed as win-rates related to general preferences

Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 57

Adapting Large Language Models via Reading Comprehension

Paper • 2309.09530 • Published Sep 18, 2023 • 69
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 57
Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs

Paper • 2404.05719 • Published Apr 8 • 57

Chain-of-Thought Reasoning Without Prompting

Paper • 2402.10200 • Published Feb 15 • 90
Self-Discover: Large Language Models Self-Compose Reasoning Structures

Paper • 2402.03620 • Published Feb 6 • 102
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 57
Do language models plan ahead for future tokens?

Paper • 2404.00859 • Published Apr 1 • 2

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