Small language models enable rapid and accurate extraction of structured data from unstructured text: An example with plants and their specialized metabolites.

ABSTRACT: Transformer-based large language models are receiving considerable attention because of their ability to analyse scientific literature. Small language models (SLMs), however, also have potential in this area as they have smaller compute footprints and allow users to keep data in-house. Here, we quantitatively evaluate the ability of SLMs to: (i) score references according to project-specific relevance and (ii) extract and structuring data from unstructured sources (scientific abstracts). By comparing SLMs' outputs against those of a human on hundreds of abstracts, we found that (i) SLMs can effectively filter literature and extract structured information relatively accurately (error rates as low as 10%), but not with perfect yield (as low as 50% in some cases), (ii) that there are tradeoff

SUBMITTER: Busta L 

PROVIDER: S-EPMC12451238 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature