Yufan Wu


2023

pdf bib
Hi-ToM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models
Yufan Wu | Yinghui He | Yilin Jia | Rada Mihalcea | Yulong Chen | Naihao Deng
Findings of the Association for Computational Linguistics: EMNLP 2023

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 also incorporate a new deception mechanism in ToM reasoning. 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.