Related Experiment Video
Updated: Jul 3, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Data science opportunities of large language models for neuroscience and biomedicine
Danilo Bzdok1, Andrew Thieme2, Oleksiy Levkovskyy2
1Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada; TheNeuro - Montreal Neurological Institute (MNI), Department of Biomedical Engineering, McGill University, Montreal, QC, Canada.
Abstract:
Large language models (LLMs) are a new asset class in the machine-learning landscape. Here we offer a primer on defining properties of these modeling techniques. We then reflect on new modes of investigation in which LLMs can be used to reframe classic neuroscience questions to deliver fresh answers. We reason that LLMs have the potential to (1) enrich neuroscience datasets by adding valuable meta-information, such as advanced text sentiment, (2) summarize vast information sources to overcome divides between siloed neuroscience communities, (3) enable previously unthinkable fusion of disparate information sources relevant to the brain, (4) help deconvolve which cognitive concepts most usefully grasp phenomena in the brain, and much more.

