Theory of Mind (ToM)-the cognitive ability to reason about mental states of
ourselves and others, is the foundation of social interaction. Although ToM
comes naturally to humans, it poses a significant challenge to even the most
advanced Large Language Models (LLMs). Due to the complex logical chains in ToM
reasoning, especially in higher-order ToM questions, simply utilizing reasoning
methods like Chain of Thought (CoT) will not improve the ToM capabilities of
LLMs. We present TimeToM, which constructs a temporal space and uses it as the
foundation to improve the ToM capabilities of LLMs in multiple scenarios.
Specifically, within the temporal space, we construct Temporal Belief State
Chain (TBSC) for each character and inspired by the cognition perspective of
the social world model, we divide TBSC into self-world beliefs and social world
beliefs, aligning with first-order ToM (first-order beliefs) and higher-order
ToM (higher-order beliefs) questions, respectively. Moreover, we design a novel
tool-belief solver that, by considering belief communication between characters
in temporal space, can transform a character's higher-order beliefs into
another character's first-order beliefs under belief communication period.
Experimental results indicate that TimeToM can dramatically improve the
reasoning performance of LLMs on ToM questions while taking a big step towards
coherent and robust ToM reasoning.

TimeToM 使用时间空间构建了 Temporal Belief State Chain (TBSC)，并设计了一个新型的 belief solver，通过考虑角色之间的信仰交流，在信仰交流期间将角色的高阶信念转化为另一个角色的一阶信念，从而显著提高了 LLMs 对 ToM 问题的推理性能。

TimeToM: 时空是解锁大型语言模型心灵理论之门的关键

TimeToM: Temporal Space is the Key to Unlocking the Door of Large  Language Models' Theory-of-Mind

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.

人的心智理论 (TOM) 能力是人类推理自己及他人心智状态的能力，在智力发展、语言理解及认知过程中起着关键作用。本研究引入了 HI-TOM，较高阶的心智理论基准。实验结果表明，基于大型语言模型 (LLM) 的性能在较高阶心智理论任务上出现下降，展示了现有 LLM 的局限性。我们对 LLM 不同失败案例进行了详细分析，并就我们的发现对自然语言处理未来的影响进行了讨论。