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arXiv AI By Camila Kolling, Mariya Toneva

Decomposing Brain-LLM Alignment with Interpretable Semantic Representations

Read the original on arXiv AI →

The study investigates how a large language model (LLaMA2‑7B) aligns with human brain activity during naturalistic language comprehension by using SCRIBE, an interpretable semantic representation of 65 neurocognitively grounded properties. Variance partitioning on fMRI data from seven participants reveals that shared variance between LLaMA2‑7B and SCRIBE dominates across the brain, yet both representations also contribute unique variance. Further analysis shows that concrete semantic properties correlate more strongly with shared variance, while abstract properties do not show increased unique variance, and ablation experiments demonstrate that multiple semantic dimensions cumulatively influence both shared and unique brain-predictive variance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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