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Structured information extraction from scientific text with large language models

Author

Listed:
  • John Dagdelen

    (Lawrence Berkeley National Laboratory
    University of California)

  • Alexander Dunn

    (Lawrence Berkeley National Laboratory
    University of California)

  • Sanghoon Lee

    (Lawrence Berkeley National Laboratory
    University of California)

  • Nicholas Walker

    (Lawrence Berkeley National Laboratory)

  • Andrew S. Rosen

    (Lawrence Berkeley National Laboratory
    University of California)

  • Gerbrand Ceder

    (Lawrence Berkeley National Laboratory
    University of California)

  • Kristin A. Persson

    (Lawrence Berkeley National Laboratory
    University of California)

  • Anubhav Jain

    (Lawrence Berkeley National Laboratory)

Abstract

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 composition/phase/morphology/application information extraction. Records are extracted from single sentences or entire paragraphs, and the output can be returned as simple English sentences or a more structured format such as a list of JSON objects. This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers.

Suggested Citation

  • John Dagdelen & Alexander Dunn & Sanghoon Lee & Nicholas Walker & Andrew S. Rosen & Gerbrand Ceder & Kristin A. Persson & Anubhav Jain, 2024. "Structured information extraction from scientific text with large language models," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45563-x
    DOI: 10.1038/s41467-024-45563-x
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    References listed on IDEAS

    as
    1. Vahe Tshitoyan & John Dagdelen & Leigh Weston & Alexander Dunn & Ziqin Rong & Olga Kononova & Kristin A. Persson & Gerbrand Ceder & Anubhav Jain, 2019. "Unsupervised word embeddings capture latent knowledge from materials science literature," Nature, Nature, vol. 571(7763), pages 95-98, July.
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