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Theory of Mind in Large Language Models: Assessment and Enhancement

Paper · 2025 · Ruirui Chen, Weifeng Jiang, Chengwei Qin et al.

Summary

This ACL 2025 survey by Chen et al. examines how well large language models handle Theory of Mind (ToM), the capacity to infer beliefs, intentions, emotions and other mental states of agents. It also reviews how that capacity can be improved. The authors note an open debate: some studies report promising ToM behaviour in LLMs, while others find it brittle and shallow. The only earlier review of ToM benchmarks stopped at 2023.

The survey has two parts:

  • Evaluation: It covers story-based benchmarks from 2023–2024, split into text-only (narrative and conversational) and multimodal sets. It maps their coverage onto the seven mental-state categories of the ATOMS framework and traces how these benchmarks have evolved.
  • Enhancement: It groups improvement methods into prompt-only approaches and approaches that add other techniques, such as fine-tuning or inverse planning.

The main finding is that both benchmarks and methods concentrate heavily on beliefs, and LLMs still lack robust ToM. The survey closes with research directions that offer a structured entry point for work on socially aware models.

Key points

  • Benchmarks reviewed: 13 benchmarks are compared by mental-state coverage. They include ToMi, HI-TOM, TOMCHALLENGES, FANTOM, BigToM, OpenToM, NegotiationToM, TOMBENCH, SimpleToM, MMToM-QA and MuMA-ToM. Beliefs are the only category every benchmark covers.
  • Benchmark design trends: HI-TOM extends belief questions from zeroth to fourth order. ToMi introduced "story accuracy," which counts a story as solved only if all of its questions are answered correctly. FANTOM uses LLM-generated conversations instead of narratives to detect "illusory ToM."
  • Enhancement methods:
    • Prompt-only: SYMBOLICTOM, SIMTOM, PercepToM, TIMETOM.
    • Fine-tuning: ToM-LM, BIP-ALM.
    • Inverse planning: BIP-ALM, LIMP.
    • Interactive benchmarks (e.g., MINDCRAFT, G-DRAGON) and methods from the small-model era are covered in the appendices.
  • Future directions:
    • Cover a wider range of mental states, for example by adapting the 127 child ToM tests compiled by Fu et al. (2023).
    • Develop joint or feedback-based methods to reduce error propagation in pipelines.
    • Handle multimodal input without losing information when video is converted to text.
    • Use curriculum learning to tackle tasks of increasing complexity.
    • Build active, agentic benchmarks.
  • Evaluation of reasoning: The authors argue that checking answer correctness alone is insufficient. Benchmarks should also assess reasoning processes and probe models' internal representations of mental states.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Nanyang Technological UniversitySingapore · Singapore156 works · 25,644 citations🏛️Agency for Science, Technology and ResearchSingapore · Singapore30 works · 14,235 citations🎓The Hong Kong University of Science and Technology (Guangzhou)Guangzhou · China53 works · 1,367 citations🔬Institute of High Performance ComputingSingapore · Singapore5 works · 169 citations

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 worksPsychology · 1,552 worksDevelopmental and Educational Psychology · 499 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Ruirui Chen, Weifeng Jiang, Chengwei Qin and Cheston Tan. 2025. Theory of Mind in Large Language Models: Assessment and Enhancement. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.acl-long.1522

@inproceedings{chen2025theory,
  title = {{Theory of Mind in Large Language Models: Assessment and Enhancement}},
  author = {Ruirui Chen, Weifeng Jiang, Chengwei Qin and Cheston Tan},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.1522},
  url = {https://doi.org/10.18653/v1/2025.acl-long.1522}
}

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