10 papers
Preserving Historical Truth: Detecting Historical Revisionism in Large Language Models
Francesco Ortu, Joeun Yook, Punya Syon Pandey +5
Large language models (LLMs) are increasingly used as sources of historical information, motivating the need for scalable audits on contested events and politically charged narrati…
GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theory
Pepijn Cobben, Xuanqiang Angelo Huang, Thao Amelia Pham +3
Frontier AI systems are increasingly capable and deployed in high-stakes multi-agent environments. However, existing AI safety benchmarks largely evaluate single agents, leaving mu…
IV Co-Scientist: Multi-Agent LLM Framework for Causal Instrumental Variable Discovery
Ivaxi Sheth, Zhijing Jin, Bryan Wilder +2
In the presence of confounding between an endogenous variable and the outcome, instrumental variables (IVs) are used to isolate the causal effect of the endogenous variable. Identi…
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…
A Randomized Controlled Trial on Anonymizing Reviewers to Each Other in Peer Review Discussions
Charvi Rastogi, Xiangchen Song, Zhijing Jin +4
Peer review often involves reviewers submitting their independent reviews, followed by a discussion among reviewers of each paper. A question among policymakers is whether the revi…