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Prof
Max Planck Institute of Software Systems; Hebrew University; École Normale Supérieure
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Schedule
Friday, November 29, 2024
3:00 PM Europe/Vienna
Domain
NeuroscienceHost
Brain Prize Webinar Series 2024
Duration
70 minutes
This webinar convened researchers at the intersection of Artificial Intelligence and Neuroscience to investigate how large language models (LLMs) can serve as valuable “model organisms” for understanding human language processing. Presenters showcased evidence that brain recordings (fMRI, MEG, ECoG) acquired while participants read or listened to unconstrained speech can be predicted by representations extracted from state-of-the-art text- and speech-based LLMs. In particular, text-based LLMs tend to align better with higher-level language regions, capturing more semantic aspects, while speech-based LLMs excel at explaining early auditory cortical responses. However, purely low-level features can drive part of these alignments, complicating interpretations. New methods, including perturbation analyses, highlight which linguistic variables matter for each cortical area and time scale. Further, “brain tuning” of LLMs—fine-tuning on measured neural signals—can improve semantic representations and downstream language tasks. Despite open questions about interpretability and exact neural mechanisms, these results demonstrate that LLMs provide a promising framework for probing the computations underlying human language comprehension and production at multiple spatiotemporal scales.
Maryia Toneva, Ariel Goldstein, Jean-Remi King
Prof
Max Planck Institute of Software Systems; Hebrew University; École Normale Supérieure
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