Human interactions are deeply rooted in the interplay of thoughts, beliefs,
and desires made possible by Theory of Mind (ToM): our cognitive ability to
understand the mental states of ourselves and others. Although ToM may come
naturally to us, emulating it presents a challenge to even the most advanced
Large Language Models (LLMs). Recent improvements to LLMs' reasoning
capabilities from simple yet effective prompting techniques such as
Chain-of-Thought have seen limited applicability to ToM. In this paper, we turn
to the prominent cognitive science theory "Simulation Theory" to bridge this
gap. We introduce SimToM, a novel two-stage prompting framework inspired by
Simulation Theory's notion of perspective-taking. To implement this idea on
current ToM benchmarks, SimToM first filters context based on what the
character in question knows before answering a question about their mental
state. Our approach, which requires no additional training and minimal
prompt-tuning, shows substantial improvement over existing methods, and our
analysis reveals the importance of perspective-taking to Theory-of-Mind
capabilities. Our findings suggest perspective-taking as a promising direction
for future research into improving LLMs' ToM capabilities.

通过仿真理论的视角引导框架 SimToM，在 Theory of Mind (ToM) 的背景下，改进了大型语言模型（LLMs）的推理能力，无需额外训练和大量提示微调，从而实现了对 ToM 能力的显著提升。