@inproceedings{neveditsin-etal-2025-evaluating,
    title = "Evaluating Structured Output Robustness of Small Language Models for Open Attribute-Value Extraction from Clinical Notes",
    author = "Neveditsin, Nikita  and
      Lingras, Pawan  and
      Mago, Vijay Kumar",
    editor = "Zhao, Jin  and
      Wang, Mingyang  and
      Liu, Zhu",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-srw.19/",
    pages = "286--296",
    ISBN = "979-8-89176-254-1",
    abstract = "We present a comparative analysis of the parseability of structured outputs generated by small language models for open attribute-value extraction from clinical notes. We evaluate three widely used serialization formats: JSON, YAML, and XML, and find that JSON consistently yields the highest parseability. Structural robustness improves with targeted prompting and larger models, but declines for longer documents and certain note types. Our error analysis identifies recurring format-specific failure patterns. These findings offer practical guidance for selecting serialization formats and designing prompts when deploying language models in privacy-sensitive clinical settings."
}