GPT-4o Lacks Core Features of Theory of Mind

TL;DR

This paper critically examines whether GPT-4o and similar LLMs truly possess a Theory of Mind, revealing they lack a coherent, consistent mental model despite performing well on some social tasks.

Contribution

It introduces a new evaluation framework based on cognitive definitions of ToM to test the internal mental models of LLMs.

Findings

LLMs succeed in simple ToM tasks but fail in logically equivalent tests.

LLMs show low consistency between predicted actions and mental state inferences.

Social proficiency in LLMs does not stem from a genuine, domain-general ToM.

Abstract

Do Large Language Models (LLMs) possess a Theory of Mind (ToM)? Research into this question has focused on evaluating LLMs against benchmarks and found success across a range of social tasks. However, these evaluations do not test for the actual representations posited by ToM: namely, a causal model of mental states and behavior. Here, we use a cognitively-grounded definition of ToM to develop and test a new evaluation framework. Specifically, our approach probes whether LLMs have a coherent, domain-general, and consistent model of how mental states cause behavior -- regardless of whether that model matches a human-like ToM. We find that even though LLMs succeed in approximating human judgments in a simple ToM paradigm, they fail at a logically equivalent task and exhibit low consistency between their action predictions and corresponding mental state inferences. As such, these findings suggest that the social proficiency exhibited by LLMs is not the result of a domain-general or consistent ToM.