Large Language Models can extract morphological data from taxonomic descriptions, but their stochastic nature makes automation challenging: a test on Australian Asteraceae.

ABSTRACT: Morphological data are critical for taxonomy, evolutionary biology, ecology, and species identification. However, no widely used central database for morphological data exists as it does for DNA sequences or specimen data. Most of these data are "locked up" in taxonomic literature. Various scripted and Natural Language Processing approaches have been explored to automate the extraction of morphological data from taxonomic descriptions. Here, we explore the feasibility of using Large Language Models (LLMs) and Optical Character Recognition (OCR) to rapidly extract data for 51 morphological characters of Australian native and introduced Asteraceae (daisy family) to populate a taxon × character table. ChatGPT 4o was used to process all 1,121 descriptions, which, following currently accepted t

SUBMITTER: Schmidt-Lebuhn AN 

PROVIDER: S-EPMC12381580 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature