Increasing alignment of large language models with language processing in the human brain

doi:10.1038/s43588-025-00863-0

Increasing alignment of large language models with language processing in the human brain

2025-09-16

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DOI：10.1038/s43588-025-00863-0

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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. Larger LLMs’ self-attention more accurately predicts readers’ regressive saccades and fMRI responses in language regions, whereas instruction tuning adds no benefit.

Keywords:

Large language models

Instruction tuning

Human brain

Naturalistic reading

Cognitive plausibility

Increasing alignment of large language models with language processing in the human brain

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