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.

我们的研究通过引入符号执行器和微调技术，改进了大型语言模型在心智理论推理中的表现，并通过对比实验证明了我们的方法对于推理能力的显著提升。