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

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