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Can large language models explore in-context?
Paper • 2403.15371 • Published • 30 -
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
Paper • 2406.04271 • Published • 29 -
CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis
Paper • 2407.13301 • Published • 54 -
WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents
Paper • 2410.07484 • Published • 50
Wayn Wang
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Data
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Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment
Paper • 2401.12474 • Published • 36 -
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
Paper • 2403.12968 • Published • 26 -
RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models
Paper • 2310.00746 • Published • 1 -
LESS: Selecting Influential Data for Targeted Instruction Tuning
Paper • 2402.04333 • Published • 3
RAG
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Can large language models explore in-context?
Paper • 2403.15371 • Published • 30 -
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
Paper • 2406.04271 • Published • 29 -
CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis
Paper • 2407.13301 • Published • 54 -
WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents
Paper • 2410.07484 • Published • 50
Data
-
Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment
Paper • 2401.12474 • Published • 36 -
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
Paper • 2403.12968 • Published • 26 -
RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models
Paper • 2310.00746 • Published • 1 -
LESS: Selecting Influential Data for Targeted Instruction Tuning
Paper • 2402.04333 • Published • 3
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