Large language models poorly capture how word order shapes representations of sentence meaning in the human brain

Large language models based on the transformer architecture are now capable of producing human-like language. But do they encode and process linguistic meaning in a human-like way? Here, we address this question by analysing 7T fMRI data from 30 participants reading 108 sentences each. These sentences are carefully designed to disentangle sentence structure from word meaning, thereby testing whether transformer representations of sentence meaning resemble those formed by the brain. Using representational similarity analysis, we compared representations from eight transformers and eleven other semantic models to brain activity in language regions of interest. We found that while transformer models match brain representations better than models that completely ignore word order, all transformers poorly match human brain representations of sentences that require sensitivity to structural relations such as semantic roles (who did what to whom). Further, transformers were significantly inferior to models explicitly designed to encode the structural relations between words. Our results provide insight into the nature of sentence representation in the brain, highlighting the critical role of sentence structure. They also cast doubt on the claim that transformers represent sentence meaning similarly to the human brain.