6 papers · 1 filter
CORE: Measuring Multi-Agent LLM Interaction Quality under Game-Theoretic Pressures
Punya Syon Pandey, Yongjin Yang, Jiarui Liu +1
Game-theoretic interactions between agents with Large Language Models (LLMs) have revealed many emergent capabilities, yet the linguistic diversity of these interactions has not be…
Difficulty-Based Preference Data Selection by DPO Implicit Reward Gap
Xuan Qi, Rongwu Xu, Zhijing Jin
Aligning large language models (LLMs) with human preferences is a critical challenge in AI research. While methods like Reinforcement Learning from Human Feedback (RLHF) and Direct…
Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games
David Guzman Piedrahita, Yongjin Yang, Mrinmaya Sachan +3
As large language models (LLMs) are increasingly deployed as autonomous agents, understanding their cooperation and social mechanisms is becoming increasingly important. In particu…
Revisiting Multi-Agent Debate as Test-Time Scaling: A Systematic Study of Conditional Effectiveness
Yongjin Yang, Euiin Yi, Jongwoo Ko +3
The remarkable growth in large language model (LLM) capabilities has spurred exploration into multi-agent systems, with debate frameworks emerging as a promising avenue for enhance…
Agent-to-Agent Theory of Mind: Testing Interlocutor Awareness among Large Language Models
Younwoo Choi, Changling Li, Yongjin Yang +1
As large language models (LLMs) are increasingly integrated into multi-agent and human-AI systems, understanding their awareness of both self-context and conversational partners is…
Causality for Natural Language Processing
Zhijing Jin
Causal reasoning is a cornerstone of human intelligence and a critical capability for artificial systems aiming to achieve advanced understanding and decision-making. This thesis d…