Structured information extraction from scientific text with large language models.
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ABSTRACT:
SUBMITTER: Dagdelen J
PROVIDER: S-EPMC10869356 | biostudies-literature | 2024 Feb
REPOSITORIES: biostudies-literature
Publications
Structured information extraction from scientific text with large language models.
Dagdelen John J Dunn Alexander A Lee Sanghoon S Walker Nicholas N Rosen Andrew S AS Ceder Gerbrand G Persson Kristin A KA Jain Anubhav A
Nature communications 20240215 1
Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composi ...[more]
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