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A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks

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arxiv 2502.06470 v1 pith:2OGGNQVU submitted 2025-02-10 cs.CL cs.AI

Hieu Minh "Jord" Nguyen This is my paper · ORCID
classification cs.CLcs.AI
keywords riskslanguagelargemindmodelssafetysurveytheory
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Theory of Mind (ToM), the ability to attribute mental states to others and predict their behaviour, is fundamental to social intelligence. In this paper, we survey studies evaluating behavioural and representational ToM in Large Language Models (LLMs), identify important safety risks from advanced LLM ToM capabilities, and suggest several research directions for effective evaluation and mitigation of these risks.

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Cited by 1 Pith paper

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  1. Agents Require Metacognitive and Strategic Reasoning to Succeed in the Coming Labor Markets

    cs.AI 2025-05 conditional novelty 5.0 of 10

    AI agents in future labor markets will need metacognitive and strategic reasoning because incomplete information creates adverse selection, moral hazard, and reputation effects.

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