@inproceedings{sarangi-etal-2025-decompose,
title = "Decompose-{T}o{M}: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decomposition",
author = "Sarangi, Sneheel and
Elgarf, Maha and
Salam, Hanan",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.682/",
pages = "10228--10241",
abstract = "Theory of Mind (ToM) is the ability to under- stand and reflect on the mental states of oth- ers. Although this capability is crucial for hu- man interaction, testing on Large Language Models (LLMs) reveals that they possess only a rudimentary understanding of it. Although the most capable closed-source LLMs have come close to human performance on some ToM tasks, they still perform poorly on com- plex variations of the task that involve more structured reasoning. In this work, we utilize the concept of {\textquotedblleft}pretend-play{\textquotedblright}, or {\textquotedblleft}Simulation Theory{\textquotedblright} from cognitive psychology to propose {\textquotedblleft}Decompose-ToM{\textquotedblright}: an LLM-based inference algorithm that improves model performance on complex ToM tasks. We recursively simu- late user perspectives and decompose the ToM task into a simpler set of tasks: subject identi- fication, question-reframing, world model up- dation, and knowledge availability. We test the algorithm on higher-order ToM tasks and a task testing for ToM capabilities in a conversa- tional setting, demonstrating that our approach shows significant improvement across models compared to baseline methods while requiring minimal prompt tuning across tasks and no ad- ditional model training."
}
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<abstract>Theory of Mind (ToM) is the ability to under- stand and reflect on the mental states of oth- ers. Although this capability is crucial for hu- man interaction, testing on Large Language Models (LLMs) reveals that they possess only a rudimentary understanding of it. Although the most capable closed-source LLMs have come close to human performance on some ToM tasks, they still perform poorly on com- plex variations of the task that involve more structured reasoning. In this work, we utilize the concept of “pretend-play”, or “Simulation Theory” from cognitive psychology to propose “Decompose-ToM”: an LLM-based inference algorithm that improves model performance on complex ToM tasks. We recursively simu- late user perspectives and decompose the ToM task into a simpler set of tasks: subject identi- fication, question-reframing, world model up- dation, and knowledge availability. We test the algorithm on higher-order ToM tasks and a task testing for ToM capabilities in a conversa- tional setting, demonstrating that our approach shows significant improvement across models compared to baseline methods while requiring minimal prompt tuning across tasks and no ad- ditional model training.</abstract>
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%0 Conference Proceedings
%T Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decomposition
%A Sarangi, Sneheel
%A Elgarf, Maha
%A Salam, Hanan
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F sarangi-etal-2025-decompose
%X Theory of Mind (ToM) is the ability to under- stand and reflect on the mental states of oth- ers. Although this capability is crucial for hu- man interaction, testing on Large Language Models (LLMs) reveals that they possess only a rudimentary understanding of it. Although the most capable closed-source LLMs have come close to human performance on some ToM tasks, they still perform poorly on com- plex variations of the task that involve more structured reasoning. In this work, we utilize the concept of “pretend-play”, or “Simulation Theory” from cognitive psychology to propose “Decompose-ToM”: an LLM-based inference algorithm that improves model performance on complex ToM tasks. We recursively simu- late user perspectives and decompose the ToM task into a simpler set of tasks: subject identi- fication, question-reframing, world model up- dation, and knowledge availability. We test the algorithm on higher-order ToM tasks and a task testing for ToM capabilities in a conversa- tional setting, demonstrating that our approach shows significant improvement across models compared to baseline methods while requiring minimal prompt tuning across tasks and no ad- ditional model training.
%U https://aclanthology.org/2025.coling-main.682/
%P 10228-10241
Markdown (Informal)
[Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decomposition](https://aclanthology.org/2025.coling-main.682/) (Sarangi et al., COLING 2025)
ACL