Year: 2025
Type: conference-paper
Abstract: Theory-of-Mind (ToM), the ability to infer the mental states, goals, and preferences of others -is a core component of human social intelligence.In this work, we investigate whether Large Language Models (LLMs) exhibit ToM capabilities in the context of strategic interaction.We frame opponent modeling in negotiation as a grounded and interpretable ToM task, where a model must infer an agent's preferences by observing offer exchanges during the negotiation.We guide LLMs to interpret offer histories and infer latent utility representations, including issue and value weights.We conduct a comprehensive evaluation of state-of-the-art LLMs across multiple negotiation domains.Our results show that LLMs can successfully recover opponents unknown preferences and in some cases even outperform classical opponent modeling baselines, even without task-specific training.These findings offer new evidence of LLMs' emerging capacity for social reasoning and position opponent modeling as a practical benchmark for evaluating Theory-of-Mind in foundation models.
Authors: Emre Kuru, Anıl Doğru, Merve Doğan, Reyhan Aydoğan
Institutions: Özyeğin University, Delft University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414959212