@inproceedings{bartels-etal-2026-relations,
    title = "Relations of Linguistic Features and Medical Text Preferences are Nontrivial",
    author = "Bartels, Davis  and
      Colelough, Brandon  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-1.87/",
    pages = "1080--1088",
    ISBN = "979-8-89176-434-7",
    abstract = "We study how simple linguistic features relate to reader preferences in medical question answering. Our dataset contains answers to medical questions ranked in order of quality. We examine eight interpretable features of the answer text: length in words, average words per sentence, percentage of polysyllabic words, medical named entity density, perplexity, coherence, and dependency distance. We find substantial variation across annotators in both the strength and direction of these relationships. Answer length shows some of the strongest associations and predictive signals, but preferences are not consistent across annotators, with some favoring longer answers and others favoring shorter ones. A leave-one-out ablation study shows the relative impact on the predictive accuracy of our models. Overall, these results suggest that linguistic form can influence reader preference in medical text, but that these effects vary across readers and may be more complex than simple linear correlations."
}