Linguistic properties and model scale in brain encoding: from small to compressed language models

TL;DR

Scaling language models enhances their alignment with human brain activity, but smaller and compressed models can achieve similar brain predictivity, indicating that large size is not strictly necessary for brain-relevant representations.

Contribution

This study systematically compares the effects of model scale and compression on brain alignment, revealing that modest models and compressed variants can match larger models in neural predictivity.

Findings

3B models match larger models in brain predictivity

Compression methods largely preserve neural alignment

Brain predictivity saturates at modest model sizes

Abstract

Recent work has shown that scaling large language models (LLMs) improves their alignment with human brain activity, yet it remains unclear what drives these gains and which representational properties are responsible. Although larger models often yield better task performance and brain alignment, they are increasingly difficult to analyze mechanistically. This raises a fundamental question: what is the minimal model capacity required to capture brain-relevant representations? To address this question, we systematically investigate how constraining model scale and numerical precision affects brain alignment. We compare full-precision LLMs, small language models (SLMs), and compressed variants (quantized and pruned) by predicting fMRI responses during naturalistic language comprehension. Across model families up to 14B parameters, we find that 3B SLMs achieve brain predictivity indistinguishable from larger LLMs, whereas 1B models degrade substantially, particularly in semantic language regions. Brain alignment is remarkably robust to compression: most quantization and pruning methods preserve neural predictivity, with GPTQ as a consistent exception. Linguistic probing reveals a dissociation between task performance and brain predictivity: compression degrades discourse, syntax, and morphology, yet brain predictivity remains largely unchanged. Overall, brain alignment saturates at modest model scales and is resilient to compression, challenging common assumptions about neural scaling and motivating compact models for brain-aligned language modeling.