A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917917529997312 |
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| author | Nguyen, Hieu Minh "Jord" |
| author_facet | Nguyen, Hieu Minh "Jord" |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06470 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks Nguyen, Hieu Minh "Jord" Computation and Language Artificial Intelligence 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. |
| title | A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2502.06470 |