Do Language Models Know Who Did What to Whom?

Authors

Joseph M Denning, Xiaohan Hannah Guo, Bryor Snefjella, Idan A Blank

Published in

Open mind : discoveries in cognitive science. Volume 10. Pages 923-950. Epub Jul 07, 2026.

Abstract

Language models (LMs) are commonly criticized for not "understanding" language. However, many critiques focus on cognitive abilities that, in humans, are distinct from language processing. Here, we instead study a kind of understanding tightly linked to language: inferring "who did what to whom" (thematic roles) in a sentence. Does the central training objective of LMs-word prediction-result in sentence representations that capture thematic roles? In two experiments, we characterized sentence representations in four LMs that have been proposed as models of human language processing. The overall representational similarity of sentence pairs did not reflect whether they had the same agent/patient assignments or opposite agent/patient assignments. Furthermore, we found limited evidence that thematic role information was available in any subspace of hidden activations. However, some attention heads robustly captured thematic roles, independently of syntax. Therefore, LMs can extract thematic roles but this information influences their representations weakly.

PMID:
42472239
Bibliographic data and abstract were imported from PubMed on 19 Jul 2026.

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