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Poster Session A: Tuesday, August 12, 1:30 – 4:30 pm, de Brug & E‑Hall

From Language to Cognition: How LLMs Outgrow the Human Language Network

Badr AlKhamissi¹, Greta Tuckute², Yingtian Tang¹, Taha Osama A Binhuraib³, Antoine Bosselut⁴, Martin Schrimpf¹; ¹EPFL - EPF Lausanne, ²Massachusetts Institute of Technology, ³Georgia Institute of Technology, ⁴Swiss Federal Institute of Technology Lausanne

Presenter: Badr AlKhamissi

Large language models (LLMs) exhibit remarkable similarity to neural activity in the human language network. However, the key properties of language underlying this alignment---and how brain-like representations emerge and change across training---remain unclear. We here benchmark 34 training checkpoints spanning 300B tokens across 8 different model sizes to analyze how brain alignment relates to linguistic competence. Specifically, we find that brain alignment tracks the development of formal linguistic competence---i.e., knowledge of linguistic rules---more closely than functional linguistic competence. While functional competence, which involves world knowledge and reasoning, continues to develop throughout training, its relationship with brain alignment is weaker, suggesting that the human language network primarily encodes formal linguistic structure rather than broader cognitive functions. Notably, we find that the correlation between next-word prediction, behavioral alignment, and brain alignment fades once models surpass human language proficiency. We further show that model size is not a reliable predictor of brain alignment when controlling for the number of features. Finally, using the largest set of rigorous neural language benchmarks to date, we show that language brain alignment benchmarks remain unsaturated, highlighting opportunities for improving future models. Taken together, our findings suggest that the human language network is better modeled by formal than functional aspects of language.

Topic Area: Language & Communication

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