mlsanityData to 13 Oct 2026

Views Are My Own, but Also Yours: Benchmarking Theory of Mind Using Common Ground

arXiv 2403.02451 ↗First posted 4 Mar 2024cs.CLcited by 10

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Abstract (arXiv)

Evaluating the theory of mind (ToM) capabilities of language models (LMs) has recently received a great deal of attention. However, many existing benchmarks rely on synthetic data, which risks misaligning the resulting experiments with human behavior. We introduce the first ToM dataset based on naturally occurring spoken dialogs, Common-ToM, and show that LMs struggle to demonstrate ToM. We then show that integrating a simple, explicit representation of beliefs improves LM performance on Common-ToM.

Key facts. Cited by 10 papers in the map (data through 13 Oct 2026). Builds on 8 of its 36 references that are in the map. First posted on arXiv 4 Mar 2024.

Builds on

8 of 36 references.

References · 8

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,231
  2. 2023Large Language Models Fail on Trivial Alterations to Theory-of-Mind Taskscited by 290
  3. 2023FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactionscited by 162
  4. 2023Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Trackercited by 95
  5. 2018Evaluating Theory of Mind in Question Answeringcited by 64
  6. 2021MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Taskscited by 62
  7. 2023MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logiccited by 18
  8. 2023Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversationscited by 6

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 1; 2025: 7; 2026: 2.

10 papers.