Papers by Mathis Lamarre
Attention weights accurately predict language representations in the brain (2022.findings-emnlp)
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| Challenge: | In Transformer-based language models, the attention mechanism converts token embeddings into contextual embeddables that incorporate information from neighboring words. |
| Approach: | They analyze fMRI recordings of English language learners and extract attention weights from them to determine how well they can predict brain responses. |
| Outcome: | The resulting hidden state embeddings are more accurate than lexical embeddngs or RNN-based models. |
Encoding and Decoding Language in the Brain with Language Models (2026.eacl-tutorials)
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| Challenge: | This tutorial introduces brain-language model alignment and recent advances in brain-informed fine-tuning and brain-based fine-caching with language models. |
| Approach: | This tutorial introduces brain-language model alignment and recent advances in brain-informed fine-tuning and scaling with language models. |
| Outcome: | This tutorial introduces brain-language model alignment and recent advances in brain-informed fine-tuning and decoding with language models. |