Infusing Theory of Mind into Socially Intelligent LLM Agents
Authors: name: EunJeong Hwang, name: Yuwei Yin, name: Giuseppe Carenini, name: Peter West, name: Vered Shwartz
Organization: University of British Columbia

Theory of Mind (ToM)-an understanding of the mental states of others-is a key
aspect of human social intelligence, yet, chatbots and LLM-based social agents
do not typically integrate it. In this work, we demonstrate that LLMs that
explicitly use ToM get better at dialogue, achieving goals more effectively.
After showing that simply prompting models to generate mental states between
dialogue turns already provides significant benefit, we further introduce
ToMAgent (ToMA), a ToM-focused dialogue agent. ToMA is trained by pairing ToM
with dialogue lookahead to produce mental states that are maximally useful for
achieving dialogue goals. Experiments on the Sotopia interactive social
evaluation benchmark demonstrate the effectiveness of our method over a range
of baselines. Comprehensive analysis shows that ToMA exhibits more strategic,
goal-oriented reasoning behaviors, which enable long-horizon adaptation, while
maintaining better relationships with their partners. Our results suggest a
step forward in integrating ToM for building socially intelligent LLM agents.