Can large language models explore in-context?
Paper
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2403.15371
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Published
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32
LLM
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Advancing LLM Reasoning Generalists with Preference Trees
Paper • 2404.02078 • Published • 44 -
Long-context LLMs Struggle with Long In-context Learning
Paper • 2404.02060 • Published • 35 -
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
Paper • 2404.03715 • Published • 60 -
CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues
Paper • 2404.03820 • Published • 24 -
Social Skill Training with Large Language Models
Paper • 2404.04204 • Published • 15 -
Stream of Search (SoS): Learning to Search in Language
Paper • 2404.03683 • Published • 29 -
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Paper • 2404.05961 • Published • 64 -
Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
Paper • 2404.12253 • Published • 53 -
To Code, or Not To Code? Exploring Impact of Code in Pre-training
Paper • 2408.10914 • Published • 41 -
Predicting Rewards Alongside Tokens: Non-disruptive Parameter Insertion for Efficient Inference Intervention in Large Language Model
Paper • 2408.10764 • Published • 8 -
OLMoE: Open Mixture-of-Experts Language Models
Paper • 2409.02060 • Published • 77 -
Attention Heads of Large Language Models: A Survey
Paper • 2409.03752 • Published • 88 -
Building Math Agents with Multi-Turn Iterative Preference Learning
Paper • 2409.02392 • Published • 14 -
DSBench: How Far Are Data Science Agents to Becoming Data Science Experts?
Paper • 2409.07703 • Published • 66 -
Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale
Paper • 2409.08264 • Published • 43 -
Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers
Paper • 2409.04109 • Published • 43 -
Training Language Models to Self-Correct via Reinforcement Learning
Paper • 2409.12917 • Published • 135 -
Agent-as-a-Judge: Evaluate Agents with Agents
Paper • 2410.10934 • Published • 18