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HI-TOM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models

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arxiv 2310.16755 v1 pith:C7PPJU5K submitted 2023-10-25 cs.CL cs.AI

Yinghui He , Yufan Wu , Yilin Jia , Rada Mihalcea , Yulong Chen , Naihao Deng This is my paper
classification cs.CLcs.AI
keywords higher-orderlanguagellmsmindtheorybenchmarkhi-tomlarge
verification ladder T0 review T1 audit T2 compute T3 formal
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Theory of Mind (ToM) is the ability to reason about one's own and others' mental states. ToM plays a critical role in the development of intelligence, language understanding, and cognitive processes. While previous work has primarily focused on first and second-order ToM, we explore higher-order ToM, which involves recursive reasoning on others' beliefs. We introduce HI-TOM, a Higher Order Theory of Mind benchmark. Our experimental evaluation using various Large Language Models (LLMs) indicates a decline in performance on higher-order ToM tasks, demonstrating the limitations of current LLMs. We conduct a thorough analysis of different failure cases of LLMs, and share our thoughts on the implications of our findings on the future of NLP.

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