Self-Rewarding Language Models
Paper
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2401.10020
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Published
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144
papers
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BitNet: Scaling 1-bit Transformers for Large Language Models
Paper • 2310.11453 • Published • 96 -
ReFT: Representation Finetuning for Language Models
Paper • 2404.03592 • Published • 90 -
LLM in a flash: Efficient Large Language Model Inference with Limited Memory
Paper • 2312.11514 • Published • 258 -
LLM Augmented LLMs: Expanding Capabilities through Composition
Paper • 2401.02412 • Published • 36 -
MemGPT: Towards LLMs as Operating Systems
Paper • 2310.08560 • Published • 7 -
ReAct: Synergizing Reasoning and Acting in Language Models
Paper • 2210.03629 • Published • 14 -
Reflexion: Language Agents with Verbal Reinforcement Learning
Paper • 2303.11366 • Published • 4 -
TinyGSM: achieving >80% on GSM8k with small language models
Paper • 2312.09241 • Published • 37 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 603 -
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
Paper • 2403.09629 • Published • 74 -
MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?
Paper • 2403.14624 • Published • 51 -
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
Paper • 2404.03715 • Published • 60 -
Rho-1: Not All Tokens Are What You Need
Paper • 2404.07965 • Published • 84 -
Learn Your Reference Model for Real Good Alignment
Paper • 2404.09656 • Published • 82 -
Attention Overflow: Language Model Input Blur during Long-Context Missing Items Recommendation
Paper • 2407.13481 • Published • 9