Brain-LLM Alignment Tracks Training Data, Not Typology
A recent study investigates the alignment between brain activity and language models across different languages. The findings suggest that training-language dominance influences this alignment more than inherent language properties. This research challenges the notion of an 'English advantage' in brain-LLM alignment, highlighting the role of typological structure in syntactic processing.
- ▪The study analyzed fMRI data from 112 participants across English, Chinese, and French.
- ▪It found that training-language dominance drives the alignment pattern, rather than an inherent property of English.
- ▪A Chinese-dominant model showed better alignment with Chinese brains compared to English.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23032 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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Computer Science > Computation and Language arXiv:2605.23032 (cs) [Submitted on 21 May 2026] Title:Brain-LLM Alignment Tracks Training Data, Not Typology Authors:Dongxin Guo, Jikun Wu, Siu Ming Yiu View a PDF of the paper titled Brain-LLM Alignment Tracks Training Data, Not Typology, by Dongxin Guo and 2 other authors View PDF HTML (experimental) Abstract:Brain-LLM alignment is well established in English, yet the brain's language network is neuroanatomically universal across languages. Does alignment also generalize cross-linguistically, and what governs the variation? We test this using fMRI data from 112 participants across English, Chinese, and French (the Le Petit Prince corpus) and seven LLMs spanning English-dominant, Chinese-dominant, and multilingual architectures.
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