Understanding and attributing mental states, known as Theory of Mind (ToM), emerges as a fundamental capability for human social reasoning. While Large Language Models (LLMs) appear to possess certain ToM abilities, the mechanisms underlying these capabilities remain elusive. In this study, we discover that it is possible to linearly decode the belief status from the perspectives of various agents through neural activations of language models, indicating the existence of internal representations of self and others' beliefs. By manipulating these representations, we observe dramatic changes in the models' ToM performance, underscoring their pivotal role in the social reasoning process. Additionally, our findings extend to diverse social reasoning tasks that involve different causal inference patterns, suggesting the potential generalizability of these representations.

翻译：理解并归因心理状态（即心智理论）是人类社会推理的基础能力。尽管大语言模型（LLMs）似乎具备一定的心智理论能力，但这些能力背后的机制仍不明确。本研究发现，通过语言模型的神经激活，可以从不同主体的视角线性解码信念状态，表明模型内部存在自我与他人信念的表征。通过操控这些表征，我们观察到模型的心智理论性能发生显著变化，凸显了这些表征在社会推理过程中的关键作用。此外，我们的发现可推广至涉及不同因果推断模式的社会推理任务，提示这些表征可能具有广泛适用性。