cross-lingual-llm-brain-alignment

name: cross-lingual-llm-brain-alignment description: "Multi-lingual whole-brain encoding framework examining brain-LLM alignment across three typologically distinct languages (Mandarin, English, French). Shows that transformer-based models predict activity in widely distributed cortical functional networks (limbic, ventral attention, default mode, subcortical) across languages, revealing computational roots of cross-linguistic neural alignment with LLM representations. Activation: cross-lingual brain alignment, multilingual fMRI encoding, LLM-brain alignment, computational neurolinguistics, cross-linguistic neural representations." arxiv_id: "2605.21049" published: "2026-05-20" authors: "Ni Yang, Rui He, Philipp Homan, Iris Sommer, Davide Staub, Wolfram Hinzen" tags: [brain-llm-alignment, cross-lingual, neurolinguistics, fmri-encoding, multilingual]

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

Examines brain-LLM alignment across Mandarin, English, and French using a whole-brain encoding framework, revealing distributed cortical and subcortical overlap with shared computational principles.

Source: arXiv: 2605.21049

Core Methodology

Key Innovation

First systematic investigation of brain-LLM alignment across three typologically distinct languages (Mandarin Chinese, English, French) using whole-brain fMRI encoding, extending beyond cortical regions to subcortical structures.

Technical Framework

1. Multilingual Naturalistic Stimuli: Participants listened to naturalistic stories in Mandarin, English, and French during fMRI scanning
2. Transformer-Based Encoding Models: Extract representations from multiple layers of LLMs (e.g., GPT-2, Llama) trained on each language
3. Whole-Brain Voxelwise Modeling: Predict BOLD activity for each voxel across the entire brain using ridge regression
4. Cross-Linguistic Alignment Analysis: Compare encoding performance across languages to identify shared vs. language-specific neural patterns
5. Subcortical Investigation: Extend beyond cortex to examine limbic, ventral attention, default mode network, and subcortical regions
6. Computational Root Analysis: Decompose which linguistic features (syntax, semantics, phonology) drive alignment patterns

Key Results

• Cross-linguistic consistency: LLM-brain alignment generalizes across typologically distinct languages
• Distributed cortical networks: Alignment spans limbic, ventral attention, default mode, and subcortical regions (not just classical language cortex)
• Shared computational principles: Common features across languages drive neural alignment
• Subcortical contributions: Subcortical regions show significant alignment not previously reported
• Layer-specific patterns: Transformer depth correlates with different functional networks across languages

Applications

• Multilingual neuroscience: Study how the human brain processes different languages at the neural level
• Brain-LLM alignment validation: Test whether alignment generalizes beyond single-language settings
• Cross-linguistic NLP and clinical applications for bilingual/multilingual populations
• Subcortical language processing: Investigate subcortical contributions to language comprehension

Activation Keywords

• cross-lingual brain alignment
• multilingual fMRI encoding
• LLM-brain alignment
• computational neurolinguistics
• whole-brain encoding model
• subcortical language processing
• cross-linguistic neural representations
• transformer language models brain

Related Skills

• sparse-autoencoder-brain-llm-topography
• brain-llm-key-neurons-grammar
• brain-llm-alignment-training-data
• mllm-brain-alignment-task-probing
• lpact-brain-lm-alignment-evaluation