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

October 25, 2023 ยท Declared Dead ยท ๐Ÿ› Conference on Empirical Methods in Natural Language Processing

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Authors Yinghui He, Yufan Wu, Yilin Jia, Rada Mihalcea, Yulong Chen, Naihao Deng arXiv ID 2310.16755 Category cs.CL: Computation & Language Cross-listed cs.AI Citations 48 Venue Conference on Empirical Methods in Natural Language Processing Last Checked 4 months ago
Abstract
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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