@inproceedings{salem-etal-2026-biocoref,
    title = "{B}io{C}oref: Benchmarking Biomedical Coreference Resolution with {LLM}s",
    author = "Salem, Nourah  and
      White, Elizabeth  and
      Bada, Michael  and
      Hunter, Lawrence",
    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-1.42/",
    pages = "519--530",
    ISBN = "979-8-89176-434-7",
    abstract = "Coreference resolution in biomedical texts presents unique challenges due to complex domain-specific terminology, high ambiguity in mention forms, and long-distance dependencies between coreferring expressions. In this work, we present a comprehensive evaluation of generative large language models (LLMs) for coreference resolution in the biomedical domain. Using the CRAFT corpus as our benchmark, we assess the LLMs' performance with four prompting experiments that vary in their use of local, contextual enrichment, and domain-specific cues such as abbreviations and entity dictionaries."
}