Unveiling Theory of Mind in Large Language Models: A Parallel to Single
  Neurons in the Human Brain
Mohsen Jamali Ziv M. Williams Jing Cai
With their recent development, large language models (LLMs) have been found
to exhibit a certain level of Theory of Mind (ToM), a complex cognitive
capacity that is related to our conscious mind and that allows us to infer
another's beliefs and perspective. While human ToM capabilities are believed to
derive from the neural activity of a broadly interconnected brain network,
including that of dorsal medial prefrontal cortex (dmPFC) neurons, the precise
processes underlying LLM's capacity for ToM or their similarities with that of
humans remains largely unknown. In this study, we drew inspiration from the
dmPFC neurons subserving human ToM and employed a similar methodology to
examine whether LLMs exhibit comparable characteristics. Surprisingly, our
analysis revealed a striking resemblance between the two, as hidden embeddings
(artificial neurons) within LLMs started to exhibit significant responsiveness
to either true- or false-belief trials, suggesting their ability to represent
another's perspective. These artificial embedding responses were closely
correlated with the LLMs' performance during the ToM tasks, a property that was
dependent on the size of the models. Further, the other's beliefs could be
accurately decoded using the entire embeddings, indicating the presence of the
embeddings' ToM capability at the population level. Together, our findings
revealed an emergent property of LLMs' embeddings that modified their
activities in response to ToM features, offering initial evidence of a parallel
between the artificial model and neurons in the human brain.