# MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic

arXiv: https://arxiv.org/abs/2305.03353 · HTML: https://mlsanity.com/paper/2305.03353/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Annotation Artifacts in Natural Language Inference Data](https://mlsanity.com/paper/1803.02324/) (2018)
- [Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks](https://mlsanity.com/paper/1502.05698/) (2015)
- [Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks](https://mlsanity.com/paper/2302.08399/) (2023)
- [Transformers as Soft Reasoners over Language](https://mlsanity.com/paper/2002.05867/) (2020)
- [On the Paradox of Learning to Reason from Data](https://mlsanity.com/paper/2205.11502/) (2022)
- [Evaluating Theory of Mind in Question Answering](https://mlsanity.com/paper/1808.09352/) (2018)
- [Boosting Theory-of-Mind Performance in Large Language Models via Prompting](https://mlsanity.com/paper/2304.11490/) (2023)
- [MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks](https://mlsanity.com/paper/2109.06275/) (2021)
- [Perturbation Augmentation for Fairer NLP](https://mlsanity.com/paper/2205.12586/) (2022)
- [Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review](https://mlsanity.com/paper/2202.12205/) (2022)
- [Pushing the Limits of Rule Reasoning in Transformers through Natural Language Satisfiability](https://mlsanity.com/paper/2112.09054/) (2021)
- [Probing neural language models for understanding of words of estimative probability](https://mlsanity.com/paper/2211.03358/) (2022)

## Built on by (most cited first, 18 of 18)

- [Theory of Mind in Large Language Models: Examining Performance of 11 State-of-the-Art models vs. Children Aged 7-10 on Advanced Tests](https://mlsanity.com/paper/2310.20320/) (2023)
- [Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models](https://mlsanity.com/paper/2310.19619/) (2023)
- [ToMChallenges: A Principle-Guided Dataset and Diverse Evaluation Tasks for Exploring Theory of Mind](https://mlsanity.com/paper/2305.15068/) (2023)
- [Theory of Mind in Large Language Models: Assessment and Enhancement](https://mlsanity.com/paper/2505.00026/) (2025)
- [A Notion of Complexity for Theory of Mind via Discrete World Models](https://mlsanity.com/paper/2406.11911/) (2024)
- [OdysSim: Building Foundation Models for Human Behavior Simulation](https://mlsanity.com/paper/2606.14199/) (2026)
- [Views Are My Own, but Also Yours: Benchmarking Theory of Mind Using Common Ground](https://mlsanity.com/paper/2403.02451/) (2024)
- [Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations](https://mlsanity.com/paper/2311.01273/) (2023)
- [Enhancing Zero-shot Chain of Thought Prompting via Uncertainty-Guided Strategy Selection](https://mlsanity.com/paper/2412.00353/) (2024)
- [CogToM: A Comprehensive Theory of Mind Benchmark inspired by Human Cognition for Large Language Models](https://mlsanity.com/paper/2601.15628/) (2026)
- [Logical forms complement probability in understanding language model (and human) performance](https://mlsanity.com/paper/2502.09589/) (2025)
- [ToM-LM: Delegating Theory of Mind Reasoning to External Symbolic Executors in Large Language Models](https://mlsanity.com/paper/2404.15515/) (2024)
- [ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning](https://mlsanity.com/paper/2305.14740/) (2023)
- [MindDial: Belief Dynamics Tracking with Theory-of-Mind Modeling for Situated Neural Dialogue Generation](https://mlsanity.com/paper/2306.15253/) (2023)
- [Enhancing Financial Sentiment Analysis with Expert-Designed Hint](https://mlsanity.com/paper/2409.17448/) (2024)
- [Distributed Partial Information Puzzles: Examining Common Ground Construction Under Epistemic Asymmetry](https://mlsanity.com/paper/2603.05450/) (2026)
- [Beyond Social Pressure: Benchmarking Epistemic Attack in Large Language Models](https://mlsanity.com/paper/2604.07749/) (2026)
- [Same Formulas, Different Semantics: Do Language Models Follow Modal Logic Specifications?](https://mlsanity.com/paper/2608.05097/) (2026)

## Abstract (arXiv)

Theory of Mind (ToM) is a critical component of intelligence but its assessment remains the subject of heated debates. Prior research applied human ToM assessments to natural language processing models using either human-created standardized tests or rule-based templates. However, these methods primarily focus on simplistic reasoning and require further validation. Here, we leverage dynamic epistemic logic to isolate a particular component of ToM and to generate controlled problems. We also introduce new verbalization techniques to express these problems in English natural language. Our findings indicate that some language model scaling (from 70M to 6B and 350M to 174B) does not consistently yield results better than random chance. While GPT-4 demonstrates superior epistemic reasoning capabilities, there is still room for improvement. Our code and datasets are publicly available (https://huggingface.co/datasets/sileod/mindgames , https://github.com/sileod/llm-theory-of-mind )