Plant blindness and diversity in AI language models.

ChatGPT artificial intelligence models diversity large language models plant blindness

Journal

Trends in plant science
ISSN: 1878-4372
Titre abrégé: Trends Plant Sci
Pays: England
ID NLM: 9890299

Informations de publication

Date de publication:
10 2023
Historique:
received: 23 05 2023
revised: 13 06 2023
accepted: 20 06 2023
medline: 18 9 2023
pubmed: 5 8 2023
entrez: 4 8 2023
Statut: ppublish

Résumé

Large language models (LLMs) will benefit science by accelerating task performance. We explored whether answers generated by ChatGPT (generative pretrained transformer) to questions of biology are sufficiently diverse. 'Plant awareness' in ChatGPT answers was found to be highly variable, illustrating the importance of scientists being involved in validating the data and methods used to train artificial intelligence (AI) models.

Identifiants

pubmed: 37541814
pii: S1360-1385(23)00207-8
doi: 10.1016/j.tplants.2023.06.016
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1095-1097

Informations de copyright

Copyright © 2023 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests The authors declare no conflicts of interest.

Auteurs

Anja Geitmann (A)

Faculty of Agricultural and Environmental Sciences, McGill University, Montreal, QC, Canada. Electronic address: geitmann.aes@mcgill.ca.

Amir J Bidhendi (AJ)

Faculty of Agricultural and Environmental Sciences, McGill University, Montreal, QC, Canada.

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Classifications MeSH