@inproceedings{xu-etal-2026-post,
    title = "Post Hoc Agentic Refinement for Improving Precision in Multilingual Clinical Text De-identification",
    author = "Xu, Justin  and
      Johnson, Alistair  and
      Lin, Thomas  and
      Eyre, David  and
      Quispe, Rodolfo",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-1.11/",
    pages = "115--127",
    ISBN = "979-8-89176-434-7",
    abstract = "De-identification systems prioritize recall to protect privacy, but excessive over-tagging reduces data utility. We propose an agentic refiner that reviews high-recall annotations using lightweight tools (validation functions, adaptive context retrieval, persistent to-do state, and modular review skills) to improve precision while minimizing recall loss. Experiments across three multilingual datasets show that the agent achieves significant improvements to binary precision. To support fine-grained analysis, we further introduce a synthetic error dataset of common and systemic failure modes, on which the agent corrects 99{\%} of injected errors in the medical datasets. Our results suggest that agent-based refinement provides a flexible and effective mechanism for improving de-identification precision as a modular extension to existing high-recall systems."
}