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ToMChallenges: A Principle-Guided Dataset and Diverse Evaluation Tasks for Exploring Theory of Mind

Paper · 2023 · Xiaomeng Ma, Lingyu Gao and Qihui Xu

Summary

Studies disagree on whether large language models have Theory of Mind (ToM). The authors argue that much of this disagreement comes from inconsistent and sometimes invalid testing. Earlier work used a few examples in a single prompt format, or used tasks that tap empathy or social attribution rather than ToM. Drawing on developmental psychology, they propose two criteria a valid test should meet: mentalizing, meaning that mental states are inferred from physical events, and false-belief understanding, meaning that one's own belief is kept separate from another person's.

They introduce ToMChallenges, a dataset built from templates of the classic Sally-Anne and Smarties false-belief tests. Each story is probed with six question types and presented in six prompt formats. Because free-form answers are costly to grade by hand, they also built a GPT-4-based auto-grader, which proved highly accurate.

They tested davinci, turbo and gpt-4. Accuracy shifted with the prompt format, question type and story, so none of the models handles ToM tasks robustly. gpt-4 was the strongest and most stable. The work shows that ToM claims about LLMs should only be made after checking many prompts and tasks.

Key points

  • The dataset contains 30 template-generated story variants of the Sally-Anne and Smarties tests. Questions cover reality, belief, and first- and second-order belief. Data and code are released at github.com/xiaomeng-ma/ToMChallenges.
  • The six evaluation formats are multiple choice, fill-in-the-blank, true/false, chain-of-thought true/false, Q&A and text completion.
  • Performance was highly uneven:
    • On Sally-Anne, davinci scored 0.5 on most formats.
    • On Smarties, turbo fell to 0.34–0.46 on the true/false, chain-of-thought true/false and Q&A formats.
    • gpt-4 ranged roughly from 0.84 to 1.0 across formats.
  • Error analysis of the Q&A and completion outputs found three error types:
    • genuine ToM failures, such as davinci's 136 on Sally-Anne;
    • overly conservative refusals to infer a belief, such as turbo's 114 on Smarties;
    • hallucinations, meaning details not supported by the story.
  • A stated limitation is that no human baseline was collected for comparison.

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

🎓Toyota Technological Institute at ChicagoChicago · United States33 works · 13,261 citations🎓The Graduate Center, CUNYNew York · United States13 works · 207 citations

Also on the byline: Basque Center on Cognition, Brain and Language.

 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

Cite

Cite as Xiaomeng Ma, Lingyu Gao and Qihui Xu. 2023. ToMChallenges: A Principle-Guided Dataset and Diverse Evaluation Tasks for Exploring Theory of Mind. arXiv:2305.15068.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2305.15068 · arXiv 2305.15068

@misc{ma2023tomchallenges,
  title = {{ToMChallenges: A Principle-Guided Dataset and Diverse Evaluation Tasks for Exploring Theory of Mind}},
  author = {Xiaomeng Ma, Lingyu Gao and Qihui Xu},
  year = {2023},
  doi = {10.48550/arxiv.2305.15068},
  eprint = {2305.15068},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2305.15068}
}

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