Divergences between Language Models and Human Brains
Do machines and humans process language in similar ways? Recent research has hinted in the affirmative, finding that brain signals can be effectively predicted using the internal representations of language models (LMs). Although such results are thought to reflect shared computational principles between LMs and human brains, there are also clear differences in how LMs and humans represent and use language. In this work, we systematically explore the divergences between human and machine language processing by examining the differences between LM representations and human brain responses to language as measured by Magnetoencephalography (MEG) across two datasets in which subjects read and listened to narrative stories. Using a data-driven approach, we identify two domains that are not captured well by LMs: social/emotional intelligence and physical commonsense. We then validate these domains with human behavioral experiments and show that fine-tuning LMs on these domains can improve their alignment with human brain responses.
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