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

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 (CoT) (Wei et al., 2022) have seen limited applicability to ToM (Gandhi et al., 2023) . 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 perspectivetaking as a promising direction for future research into improving LLMs' ToM capabilities. Our code is publicly available.