@inproceedings{shimizu-etal-2025-exploring,
    title = "Exploring {LLM} Annotation for Adaptation of Clinical Information Extraction Models under Data-sharing Restrictions",
    author = "Shimizu, Seiji  and
      Shohei, Hisada  and
      Uno, Yutaka  and
      Yada, Shuntaro  and
      Wakamiya, Shoko  and
      Aramaki, Eiji",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.757/",
    pages = "14678--14694",
    ISBN = "979-8-89176-256-5",
    abstract = "In-hospital text data contains valuable clinical information, yet deploying fine-tuned small language models (SLMs) for information extraction remains challenging due to differences in formatting and vocabulary across institutions. Since access to the original in-hospital data (source domain) is often restricted, annotated data from the target hospital (target domain) is crucial for domain adaptation. However, clinical annotation is notoriously expensive and time-consuming, as it demands clinical and linguistic expertise. To address this issue, we leverage large language models (LLMs) to annotate the target domain data for the adaptation. We conduct experiments on four clinical information extraction tasks, including eight target domain data. Experimental results show that LLM-annotated data consistently enhances SLM performance and, with a larger number of annotated data, outperforms manual annotation in three out of four tasks."
}