COKE: A Cognitive Knowledge Graph for Machine Theory of Mind
This addresses the problem of social intelligence in AI for applications requiring human-like reasoning, though it is incremental as it builds on existing knowledge graph and LLM methods.
The paper tackles the lack of theory of mind (ToM) in AI systems by proposing COKE, a cognitive knowledge graph with 45k+ manually verified cognitive chains, and COLM, a generation model for cognitive reasoning, which demonstrates superior ToM ability in evaluations.
Theory of mind (ToM) refers to humans' ability to understand and infer the desires, beliefs, and intentions of others. The acquisition of ToM plays a key role in humans' social cognition and interpersonal relations. Though indispensable for social intelligence, ToM is still lacking for modern AI and NLP systems since they cannot access the human mental state and cognitive process beneath the training corpus. To empower AI systems with the ToM ability and narrow the gap between them and humans, in this paper, we propose COKE: the first cognitive knowledge graph for machine theory of mind. Specifically, COKE formalizes ToM as a collection of 45k+ manually verified cognitive chains that characterize human mental activities and subsequent behavioral/affective responses when facing specific social circumstances. In addition, we further generalize COKE using LLMs and build a powerful generation model COLM tailored for cognitive reasoning. Experimental results in both automatic and human evaluation demonstrate the high quality of COKE, the superior ToM ability of COLM, and its potential to significantly enhance social applications.