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Rethinking Theory of Mind Benchmarks for LLMs: Towards A User-Centered Perspective

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arxiv 2504.10839 v1 pith:IBG7XPTF submitted 2025-04-15 cs.HC cs.AI

classification cs.HCcs.AI
keywords limitationsapproachbenchmarkbenchmarksllmsperspectivetaskstowards
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The last couple of years have witnessed emerging research that appropriates Theory-of-Mind (ToM) tasks designed for humans to benchmark LLM's ToM capabilities as an indication of LLM's social intelligence. However, this approach has a number of limitations. Drawing on existing psychology and AI literature, we summarize the theoretical, methodological, and evaluation limitations by pointing out that certain issues are inherently present in the original ToM tasks used to evaluate human's ToM, which continues to persist and exacerbated when appropriated to benchmark LLM's ToM. Taking a human-computer interaction (HCI) perspective, these limitations prompt us to rethink the definition and criteria of ToM in ToM benchmarks in a more dynamic, interactional approach that accounts for user preferences, needs, and experiences with LLMs in such evaluations. We conclude by outlining potential opportunities and challenges towards this direction.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. When Researchers Say Mental Model/Theory of Mind of AI, What Are They Really Talking About?

    cs.HC 2025-10 conditional novelty 3.0 of 10

    Researchers' claims of AI theory of mind are really about behavioral prediction, so AI evaluation should shift from isolated cognitive tests to human-AI interaction.

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