@inproceedings{liu-etal-2026-better,
    title = "Better Generalizing to Unseen Concepts: An Evaluation Framework and An {LLM}-Based Auto-Labeled Pipeline for Biomedical Concept Recognition",
    author = "Liu, Shanshan  and
      Nishida, Noriki  and
      Cheng, Fei  and
      Tokunaga, Narumi  and
      Munne, Rumana Ferdous  and
      Yamagata, Yuki  and
      Kozaki, Kouji  and
      Utsuro, Takehito  and
      Matsumoto, Yuji",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.172/",
    pages = "3718--3734",
    ISBN = "979-8-89176-380-7",
    abstract = "Generalization to unseen concepts is a central challenge due to the scarcity of human annotations in Mention-agnostic Biomedical Concept Recognition (MA-BCR). This work makes two key contributions to systematically address this issue. First, we propose an evaluation framework built on hierarchical concept indices and novel metrics to measure generalization. Second, we explore LLM-based Auto-Labeled Data (ALD) as a scalable resource, creating a task-specific pipeline for its generation. Our research unequivocally shows that while LLM-generated ALD cannot fully substitute for manual annotations, it is a valuable resource for improving generalization, successfully providing models with the broader coverage and structural knowledge needed to approach recognizing unseen concepts. Code and datasets are available at https://github.com/bio-ie-tool/hi-ald."
}