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Re-evaluating Theory of Mind evaluation in large language models

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arxiv 2502.21098 v1 pith:TBVBJKTJ submitted 2025-02-28 cs.AI cs.CL

classification cs.AIcs.CL
keywords llmswhethermodelsbehaviorsevaluationevaluationshumanlanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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The question of whether large language models (LLMs) possess Theory of Mind (ToM) -- often defined as the ability to reason about others' mental states -- has sparked significant scientific and public interest. However, the evidence as to whether LLMs possess ToM is mixed, and the recent growth in evaluations has not resulted in a convergence. Here, we take inspiration from cognitive science to re-evaluate the state of ToM evaluation in LLMs. We argue that a major reason for the disagreement on whether LLMs have ToM is a lack of clarity on whether models should be expected to match human behaviors, or the computations underlying those behaviors. We also highlight ways in which current evaluations may be deviating from "pure" measurements of ToM abilities, which also contributes to the confusion. We conclude by discussing several directions for future research, including the relationship between ToM and pragmatic communication, which could advance our understanding of artificial systems as well as human cognition.

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  1. The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind

    cs.AI 2025-06 conditional novelty 6.0 of 10

    A new interactive language-game benchmark shows LLMs lag behind simple word-embedding baselines and that newer reasoning models regress on theory-of-mind tasks.

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