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Theory of Mind and Self-Attributions of Mentality are Dissociable in LLMs

arXiv 2603.28925 ↗First posted 30 Mar 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

Safety fine-tuning in Large Language Models (LLMs) seeks to suppress potentially harmful forms of mind-attribution such as models asserting their own consciousness or claiming to experience emotions. We investigate whether suppressing mind-attribution tendencies degrades intimately related socio-cognitive abilities such as Theory of Mind (ToM). Through safety ablation and mechanistic analyses of representational similarity, we demonstrate that LLM attributions of mind to themselves and to technological artefacts are behaviorally and mechanistically dissociable from ToM capabilities. Nevertheless, safety fine-tuned models under-attribute mind to non-human animals relative to human baselines and are less likely to exhibit spiritual belief, suppressing widely shared perspectives regarding the distribution and nature of non-human minds.

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References · 9

  1. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  2. 2024SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMscited by 57
  3. 2025Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMscited by 55
  4. 2025Training language models to be warm and empathetic makes them less reliable and more sycophanticcited by 21
  5. 2025Large Language Models Report Subjective Experience Under Self-Referential Processingcited by 19
  6. 2024LLM Theory of Mind and Alignment: Opportunities and Riskscited by 18
  7. 2025Technological folie `a deux: Feedback Loops Between AI Chatbots and Mental Illnesscited by 15
  8. 2025The Psychogenic Machine: Simulating AI Psychosis, Delusion Reinforcement and Harm Enablement in Large Language Modelscited by 11
  9. 2025Signal in the Noise: Polysemantic Interference Transfers and Predicts Cross-Model Influencecited by 4

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