ArticleNature computational science2025
Increasing alignment of large language models with language processing in the human brain.
Article in Nature computational science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Linguistics and human brain: a perspective of computational neuroscience.Cognitive neurodynamics · 2026Review
- Neural tracking of surprisal in Spanish-English bilingual children during naturalistic heritage language listening.Brain and language · 2026Article
- Article
- An automatic consult reply system for therapeutic plasma exchange using retrieval-augmented generation.Vox sanguinis · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Transformer-based large language models (LLMs) have considerably advanced our understanding of how meaning is represented in the human brain; however, the validity of increasingly large LLMs is being questioned due to their extensive training data and their ability to access context thousands of words long. In this study we investigated whether instruction tuning-another core technique in recent LLMs that goes beyond mere scaling-can enhance models' ability to capture linguistic information in the human brain. We compared base and instruction-tuned LLMs of varying sizes against human behavioral and brain activity measured with eye-tracking and functional magnetic resonance imaging during naturalistic reading. We show that simply making LLMs larger leads to a closer match with the human brain than fine-tuning them with instructions. These finding have substantial implications for understanding the cognitive plausibility of LLMs and their role in studying naturalistic language comprehension.
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Registered trials
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