Dual Alignment Between Language Model Layers and Human Sentence Processing

Researchers found that different layers of large language models align with human sentence processing in different contexts: early layers match naturalistic reading, while later layers better capture cognitive effort during syntactic ambiguity, though both still underestimate human complexity.

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Explainer

The more consequential finding is the negative one: even the best-aligned layers fall short of capturing human cognitive load, which means current interpretability work that treats LLM activations as proxies for human comprehension may be systematically overconfident. The dual-layer structure is interesting, but the persistent gap is the real story.

This connects most directly to the DiscoTrace paper covered here on April 16, which found that LLMs lack rhetorical variety and favor breadth over human-like selectivity when constructing answers. Both papers are probing the same underlying question from different angles: where, precisely, do LLM internal representations diverge from human cognition, and at what stage? Together they suggest the divergence is not a surface-level output problem but something more structural. The K-Token Merging work from the same week is adjacent in that it manipulates the very layer representations this paper analyzes, though that work is focused on efficiency rather than cognitive alignment.

Watch whether Kuribayashi or a replication team tests this layer-alignment framework on models with significantly different architectures (such as mixture-of-experts), since the dual-layer pattern could be an artifact of standard transformer depth scaling rather than a general property of LLMs.

Coverage we drew on

DiscoTrace: Representing and Comparing Answering Strategies of Humans and LLMs in Information-Seeking Question Answering · arXiv cs.CL

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MentionsKuribayashi et al. · Large Language Models

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