RoleToM: Eliciting Theory-of-Mind Abilities through Role-playing in Large Language Models

ACL ARR 2025 May Submission3691 Authors

Abstract: The ability to attribute others' mental states, known as Theory-of-Mind (ToM), is a cornerstone of social intelligence. While large language models (LLMs) have exhibited impressive performance at various tasks including role-playing, their ToM reasoning capabilities remain limited and unreliable compared to humans. Meanwhile, the potential of leveraging role-playing for enhancing social cognition remains largely unexplored. To bridge this gap, we pioneer the investigation into how role-playing influences LLMs' Theory-of-Mind capabilities. We introduce **RoleToM**, a novel approach that integrates step-by-step reasoning with role-playing, demonstrating superior ToM abilities compared to perspective-taking and chain-of-thought methods alone. Additional experiments including ablation studies of role-playing and fine-tuning Llama 3.1-8B-Instruct on RoleToM-generated data, show that structured first-person simulation can effectively improve LLMs' social intelligence capabilities and generalize across different scenarios. We hope the role-playing methodology opens potential avenues for further applications and research in LLMs' social cognition.

Paper Type: Long

Research Area: Linguistic theories, Cognitive Modeling and Psycholinguistics

Research Area Keywords: cognitive modeling, theory of mind, social reasoning

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data analysis

Languages Studied: English

Keywords: Large Language Model, Theory of Mind, Role-play

Submission Number: 3691