ToM-LM: Delegating Theory of Mind Reasoning to External Symbolic Executors in Large Language Models
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
Theory of Mind (ToM) refers to the ability of individuals to attribute mental states to others. While Large Language Models (LLMs) have shown some promise with ToM ability, they still struggle with complex ToM reasoning. Our approach leverages an external symbolic executor, specifically the SMCDEL model checker, and fine-tuning to improve the ToM reasoning ability of LLMs. In our approach, an LLM is first fine-tuned through pairs of natural language and symbolic formulation representation of ToM problems and is then instructed to generate the symbolic formulation with a one-shot in-context example. The generated symbolic formulation is then executed by the SMCDEL model checker to perform transparent and verifiable ToM reasoning and give the final result. We demonstrate that our approach, ToM-LM, shows a significant improvement over all the constructed baselines. Our study proposes a novel view about externalizing a particular component of ToM reasoning, mainly reasoning about beliefs, and suggests generalizing it to other aspects of ToM reasoning.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 14 of its 23 references that are in the map. First posted on arXiv 23 Apr 2024.
Builds on
14 of 23 references.
Closest ancestors · 2
Same direction, newest first
- 2023LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers23 Oct
- 2023Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning20 May
Other references · 12
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
- 2022Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMscited by 220
- 2023Evaluating Large Language Models in Theory of Mind Taskscited by 217
- 2023Theory of Mind for Multi-Agent Collaboration via Large Language Modelscited by 129
- 2023Boosting Theory-of-Mind Performance in Large Language Models via Promptingcited by 64
- 2022Do Large Language Models know what humans know?cited by 60
- 2023Towards A Holistic Landscape of Situated Theory of Mind in Large Language Modelscited by 32
- 2023MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logiccited by 18
- 2023Unveiling Theory of Mind in Large Language Models: A Parallel to Single Neurons in the Human Braincited by 18
Built on by
Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2023HI-TOM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models
- 2024Zero, Finite, and Infinite Belief History of Theory of Mind Reasoning in Large Language Models
- 2026Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models
- 2023Understanding Social Reasoning in Language Models with Language Models
- 2024Probing the Robustness of Theory of Mind in Large Language Models
- 2024Mind Your Theory: Theory of Mind Goes Deeper Than Reasoning
- 2024SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs
- 2025Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decomposition