@inproceedings{colelough-etal-2026-towards,
    title = "Towards Grounded Hallucination Definitions for Biomedical Question Answering with Reproducible Examples from {C}lin{IQL}ink",
    author = "Colelough, Brandon  and
      Bartels, Davis  and
      Bittner, Madeline  and
      Demner-Fushman, Dina",
    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-1.67/",
    pages = "812--842",
    ISBN = "979-8-89176-434-7",
    abstract = "Hallucinations in biomedical question answering are hard to define and compare because the literature uses overlapping and inconsistent terms. There is currently no grounded definition set that works for biomedical QA, with real examples from open-source LLMs. We introduce a layered definition of hallucinations for biomedical QA, hierarchically structured from the overarching idea of Hallucination in relation to generated model content, to source and consistency orientations, and finally to subtypes. We ground our definition taxonomy in source-attributed literature definitions and reproducible examples from REMOVED FOR REVIEW, where cases can be traced to the question, source passage, generated answer, and annotation record. We provide a framework with annotation, comparison, and error analysis to provide a clearer reference for evidence-grounded biomedical QA. We aim for this example-grounded taxonomy to support automated detection of hallucinations and their potential harmfulness."
}