Think Twice: Perspective-Taking Improves Large Language Models' Theory-of-Mind Capabilities

• URL: http://arxiv.org/abs/2311.10227v1
• Date: Thu, 16 Nov 2023 22:49:27 GMT
• Title: Think Twice: Perspective-Taking Improves Large Language Models' Theory-of-Mind Capabilities
• Authors: Alex Wilf, Sihyun Shawn Lee, Paul Pu Liang, Louis-Philippe Morency
• Abstract summary: SimToM is a novel prompting framework inspired by Simulation Theory's notion of perspective-taking. Our approach, which requires no additional training and minimal prompt-tuning, shows substantial improvement over existing methods.
• Score: 63.90227161974381
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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