@inproceedings{wilf-etal-2024-think,
title = "Think Twice: Perspective-Taking Improves Large Language Models' Theory-of-Mind Capabilities",
author = "Wilf, Alex and
Lee, Sihyun and
Liang, Paul Pu and
Morency, Louis-Philippe",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.luhme-long.451/",
doi = "10.18653/v1/2024.acl-long.451",
pages = "8292--8308",
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 (CoT) have seen limited applicability to ToM. In this paper, we turn to the prominent cognitive science theory {\textquotedblleft}Simulation Theory{\textquotedblright} 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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<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 (CoT) 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.</abstract>
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%0 Conference Proceedings
%T Think Twice: Perspective-Taking Improves Large Language Models’ Theory-of-Mind Capabilities
%A Wilf, Alex
%A Lee, Sihyun
%A Liang, Paul Pu
%A Morency, Louis-Philippe
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F wilf-etal-2024-think
%X 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) 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.
%R 10.18653/v1/2024.acl-long.451
%U https://aclanthology.org/2024.luhme-long.451/
%U https://doi.org/10.18653/v1/2024.acl-long.451
%P 8292-8308
Markdown (Informal)
[Think Twice: Perspective-Taking Improves Large Language Models’ Theory-of-Mind Capabilities](https://aclanthology.org/2024.luhme-long.451/) (Wilf et al., ACL 2024)
ACL