Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decomposition

• URL: http://arxiv.org/abs/2501.09056v1
• Date: Wed, 15 Jan 2025 18:44:01 GMT
• Title: Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decomposition
• Authors: Sneheel Sarangi, Maha Elgarf, Hanan Salam,
• Abstract summary: Theory of Mind (ToM) is the ability to understand and reflect on the mental states of others. Large Language Models (LLMs) possess only a rudimentary understanding of ToM. We propose Decompose-ToM'': an LLM-based inference algorithm that improves model performance on complex ToM tasks.
• Score: 2.089191490381739
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• Abstract: Theory of Mind (ToM) is the ability to understand and reflect on the mental states of others. Although this capability is crucial for human 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 complex 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 simulate user perspectives and decompose the ToM task into a simpler set of functions: subject identification, question-reframing, world model updation, and knowledge availability. We test the algorithm on higher-order ToM tasks and a task testing for ToM capabilities in a conversational setting, demonstrating that our approach shows significant improvement across models compared to baseline methods while requiring minimal prompt tuning across tasks and no additional model training.

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