UniToMBench: Integrating Perspective-Taking to Improve Theory of Mind in LLMs

• URL: http://arxiv.org/abs/2506.09450v1
• Date: Wed, 11 Jun 2025 06:55:40 GMT
• Title: UniToMBench: Integrating Perspective-Taking to Improve Theory of Mind in LLMs
• Authors: Prameshwar Thiyagarajan, Vaishnavi Parimi, Shamant Sai, Soumil Garg, Zhangir Meirbek, Nitin Yarlagadda, Kevin Zhu, Chris Kim,
• Abstract summary: Theory of Mind (ToM) remains a challenging area for large language models (LLMs)<n>In this paper, we introduce UniToMBench, a unified benchmark that integrates the strengths of SimToM and TOMBENCH.
• Score: 1.4304078520604593
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Theory of Mind (ToM), the ability to understand the mental states of oneself and others, remains a challenging area for large language models (LLMs), which often fail to predict human mental states accurately. In this paper, we introduce UniToMBench, a unified benchmark that integrates the strengths of SimToM and TOMBENCH to systematically improve and assess ToM capabilities in LLMs by integrating multi-interaction task designs and evolving story scenarios. Supported by a custom dataset of over 1,000 hand-written scenarios, UniToMBench combines perspective-taking techniques with diverse evaluation metrics to better stimulate social cognition in LLMs. Through evaluation, we observe that while models like GPT-4o and GPT-4o Mini show consistently high accuracy in tasks involving emotional and belief-related scenarios, with results usually above 80%, there is significant variability in their performance across knowledge-based tasks. These results highlight both the strengths and limitations of current LLMs in ToM-related tasks, underscoring the value of UniToMBench as a comprehensive tool for future development. Our code is publicly available here: https://github.com/Shamant/unifiedtombenchmark.

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