SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs

Understand

Large language models (LLMs) are increasingly tested for a "Theory of Mind" (ToM) - the ability to attribute mental states to oneself and others.

• Yet most evaluations stop at explicit belief attribution in classical toy stories or stylized tasks, leaving open the questions of whether LLMs can implicitly apply such knowledge to predict human behavior, or to judge an observed behavior, in diverse scenarios.
• We introduce SimpleToM, a benchmark that advances ToM evaluation along two novel axes.
• First, it probes multiple levels of ToM reasoning, from mental state inference (explicit ToM) to behavior prediction and judgment (applied ToM).