We address a growing debate about the extent to which large language models (LLMs) produce behavior consistent with Theory of Mind (ToM) in humans. We present EPITOME: a battery of six experiments that tap diverse ToM capacities, including belief attribution, emotional inference, and pragmatic reasoning. We elicit a performance baseline from human participants for each task. We use the dataset to ask whether distributional linguistic information learned by LLMs is sufficient to explain ToM in humans. We compare performance of five LLMs to a baseline of responses from human comprehenders. Results are mixed. LLMs display considerable sensitivity to mental states and match human performance in several tasks. Yet, they commit systematic errors in others, especially those requiring pragmatic reasoning on the basis of mental state information. Such uneven performance indicates that human-level ToM may require resources beyond distributional information.

Multimodal large language models (MLLMs) are gaining popularity as partial solutions to the “symbol grounding problem” faced by language models trained on text alone. However, little is known about whether and how these multiple modalities are integrated. We draw inspiration from analogous work in human psycholinguistics on embodied simulation, i.e., the hypothesis that language comprehension is grounded in sensorimotor representations. We show that MLLMs are sensitive to implicit visual features like object shape (e.g., “The egg was in the skillet” implies a frying egg rather than one in a shell). This suggests that MLLMs activate implicit information about object shape when it is implied by a verbal description of an event. We find mixed results for color and orientation, and rule out the possibility that this is due to models’ insensitivity to those features in our dataset overall. We suggest that both human psycholinguistics and computational models of language could benefit from cross-pollination, e.g., with the potential to establish whether grounded representations play a functional role in language processing.