A Low-Rank Space Captures Linear Alignment Between LLM and Human Neural Activity During Naturalistic Language Listening
Paper recorded by Signals 4 on 2026-10-29 in cs.CL. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30
Category: cs.CL · 自然语言处理 · first seen 2026-10-30
Abstract
Large language models (LLMs) are increasingly used as computational models of human language processing, yet it remains unclear which components of their representations drive alignment with neural activity. Here, we use ridge reduced-rank regression to identify the brain-predictive subspace of LLM representations: the representational directions that support linear prediction of neural responses.
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Cite this page: A Low-Rank Space Captures Linear Alignment Between LLM and Human Neural Activity During Naturalistic Language Listening: the #65 most recent of 628 cs.CL papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/a-low-rank-space-captures-linear-alignment-between-llm-and-human-neural-activity.html
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