@inproceedings{meoni-etal-2023-large,
    title = "Large Language Models as Instructors: A Study on Multilingual Clinical Entity Extraction",
    author = "Meoni, Simon  and
      De la Clergerie, Eric  and
      Ryffel, Theo",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bionlp-1.15",
    doi = "10.18653/v1/2023.bionlp-1.15",
    pages = "178--190",
    abstract = "In clinical and other specialized domains, data are scarce due to their confidential nature. This lack of data is a major problem when fine-tuning language models. Nevertheless, very large language models (LLMs) are promising for the medical domain but cannot be used directly in healthcare facilities due to data confidentiality issues. We explore an approach of annotating training data with LLMs to train smaller models more adapted to our problem. We show that this method yields promising results for information extraction tasks.",
}

In clinical and other specialized domains, data are scarce due to their confidential nature. This lack of data is a major problem when fine-tuning language models. Nevertheless, very large language models (LLMs) are promising for the medical domain but cannot be used directly in healthcare facilities due to data confidentiality issues. We explore an approach of annotating training data with LLMs to train smaller models more adapted to our problem. We show that this method yields promising results for information extraction tasks.
Meoni, Simon De la Clergerie, Eric Ryffel, Theo
Large Language Models as Instructors: A Study on Multilingual Clinical Entity Extraction