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cs.AI2026
LLM Active Alignment: A Nash Equilibrium Perspective
Tonghan Wang, Yuqi Pan, Xinyi Yang +3
We develop a game-theoretic framework for predicting and steering the behavior of populations of large language models (LLMs) through Nash equilibrium (NE) analysis. To avoid the i…
cs.AI2025
LLM-Powered Preference Elicitation in Combinatorial Assignment
Ermis Soumalias, Yanchen Jiang, Kehang Zhu +3
We study the potential of large language models (LLMs) as proxies for humans to simplify preference elicitation (PE) in combinatorial assignment. While traditional PE methods rely…