Cross-lingual robustness of LLM-brain alignment and its computational roots

TL;DR

This study investigates how large language models align with brain activity across multiple languages during story listening, revealing spatially robust but computationally complex neural predictivity patterns.

Contribution

It demonstrates that brain-LLM alignment is stable across languages and brain regions, but not directly explained by predictive processing or representational geometry.

Findings

Transformer models predict activity across diverse brain networks.

Cross-linguistic overlap in spatial alignment patterns is substantial.

Alignment remains stable across model layers, with limited hierarchical progression.

Abstract

Large language models (LLMs) reliably predict neural activity during language comprehension and transformer depth has been interpreted as mirroring hierarchical cortical organization. However, it remains unclear whether such alignment extends to subcortical regions, overlaps spatially across languages, and what the computational roots of such alignment are. Here, we used a multilingual, whole-brain encoding framework to examine brain-LLM alignment across three typologically distinct languages: Mandarin, English, and French during naturalistic story listening. Our results show that across languages, transformer-based models predicted activity in a distributed landscape spanning widely distributed cortical functional networks like limbic, ventral attention, default mode network, and subcortical structures. Spatial alignment patterns showed substantial cross-linguistic overlap and remained largely stable across model layers, with limited layer progression consistent with functional cortical hierarchies. Contrary to previous evidence, contextual embeddings did not outperform static embeddings. To test candidate computational explanations, we examined whether layer-wise brain scores reflect surprisal and intrinsic dimensionality, and thereby predictive processing and information compression. Neither of these two computational metrics mirrored neural alignment profiles. Our findings suggest that brain-LLM alignment is spatially robust and cross-linguistically stable but not explainable from predictive uncertainty or representational geometry. Rather than directly reflecting shared hierarchical computation, neural predictivity may primarily arise from distributed lexical-semantic correspondences that generalize across languages.