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Large language models (LLMs) have complicated internal dynamics, but inducerepresentations of words and phrases whose geometry we can study. Humanlanguage processing is also opaque, but neural response measurements canprovide (noisy) recordings of activation during listening or reading, fromwhich we can extract similar representations of words and phrases. Here westudy the extent to which the geometries induced by these representations,share similarities in the context of brain decoding. We find that the largerneural language models get, the more their representations are structurallysimilar to neural response measurements from brain imaging. Code is availableat \url{https://github.com/coastalcph/brainlm}.

Structural Similarities Between Language Models and Neural Response Measurements