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