Improving semantic understanding in speech language models via brain-tuning

• URL: http://arxiv.org/abs/2410.09230v2
• Date: Tue, 15 Oct 2024 16:39:10 GMT
• Title: Improving semantic understanding in speech language models via brain-tuning
• Authors: Omer Moussa, Dietrich Klakow, Mariya Toneva,
• Abstract summary: Speech language models align with human brain responses to natural language to an impressive degree. Current models rely heavily on low-level speech features, indicating they lack brain-relevant semantics. We address this limitation by inducing brain-relevant bias directly into the models via fine-tuning with fMRI recordings.
• Score: 19.732593005537606
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speech language models align with human brain responses to natural language to an impressive degree. However, current models rely heavily on low-level speech features, indicating they lack brain-relevant semantics which limits their utility as model organisms of semantic processing in the brain. In this work, we address this limitation by inducing brain-relevant bias directly into the models via fine-tuning with fMRI recordings of people listening to natural stories, a process we name brain-tuning. After testing it on 3 different pretrained model families, we show that brain-tuning not only improves overall alignment with new brain recordings in semantic language regions, but also reduces the reliance on low-level speech features for this alignment. Excitingly, we further show that brain-tuning leads to 1) consistent improvements in performance on a range of downstream tasks and 2) a representational space with increased semantic preference. Our results provide converging evidence, for the first time, that incorporating brain signals into the training of language models improves the models' semantic understanding.

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