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.

翻译：人类互动深深植根于思想、信念和欲望的交织中，而这得益于心理理论（Theory of Mind, ToM）——我们理解自身及他人心理状态的认知能力。尽管心理理论对人类而言可能自然而然，但对于最先进的大语言模型（LLMs）而言，模仿这一能力却构成挑战。近期，通过简单而有效的提示技术（如思维链）提升LLMs推理能力的进展，在心理理论方面的适用性仍十分有限。本文借鉴认知科学中的重要理论——“模拟理论”（Simulation Theory）——来弥合这一差距。我们提出SimToM，一种受模拟理论中视角转换概念启发的新型两阶段提示框架。为将这一理念应用于现有心理理论基准，SimToM首先基于相关角色所知晓的情境进行上下文过滤，随后再就其心理状态进行提问。我们的方法无需额外训练且只需极少的提示调优，在现有方法基础上展现出显著改进，分析结果揭示了视角转换对心理理论能力的重要性。我们的发现表明，视角转换是未来提升LLMs心理理论能力的一个有前景的研究方向。