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

ICLR 2025 Conference Submission12701 Authors

Keywords: Theory of Mind, social reasoning, LLM benchmark, mental state, behavior, judgment, false belief

TL;DR: SimpleToM is a novel Theory-of-Mind dataset which reveals the intriguing insight that frontier LLMs perform well on explicit ToM (predicting mental state), but poorly on applied ToM (predicting behavior and judgment).

Abstract: While prior work has explored whether large language models (LLMs) possess a "theory of mind" (ToM) - the ability to attribute mental states to oneself and others - there has been little work testing whether LLMs can implicitly apply such knowledge to predict behavior, or to judge whether an observed behavior is rational. Such skills are critical for appropriate interaction in social environments. Our approach to study such capabilities is to create a new dataset, called SimpleToM, containing concise, diverse stories (e.g., "The can of Pringles has moldy chips in it. Mary picks up the can in the supermarket and walks to the cashier."), each with three questions that test different degrees of ToM reasoning, asking models to predict (a) mental state ("Is Mary aware of the mold?"), (b) behavior ("Will Mary pay for the chips or report the mold?"), and (c) judgment ("Mary paid for the chips. Was that reasonable?"). To our knowledge, SimpleToM is the first dataset to systematically explore downstream reasoning requiring knowledge of mental states in realistic scenarios. Our experimental results are intriguing: While most models can reliably predict mental state on our dataset (a), they often fail to correctly predict the behavior (b), and fare even worse at judging whether given behaviors are reasonable, despite being correctly aware of the protagonist's mental state should make such secondary predictions obvious. We further show that we can help models do better at (b) and (c) via interventions such as reminding the model of its earlier mental state answer and mental-state-specific chain-of-thought prompting, raising the action prediction accuracies (e.g., from 49.5% to 93.5% for GPT-4o) and judgment accuracies (e.g., from 15.3% to 94.7% in GPT-4o). However, while this shows that models can be coaxed to perform well, it requires task-specific interventions, and the natural model performances remain low, a cautionary tale for LLM deployment. SimpleToM thus breaks new ground in probing real-world ToM reasoning, and reveals surprising, new insights about current model capabilities. We hope the dataset enables further exploration by the community into this critical area of model behavior.

Primary Area: datasets and benchmarks

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Submission Number: 12701