@inproceedings{nahian-kavuluru-2025-radqa,
    title = "{R}ad{QA}-{DPO}: A Radiology Question Answering System with Encoder-Decoder Models Enhanced by Direct Preference Optimization",
    author = "Nahian, Md Sultan Al  and
      Kavuluru, Ramakanth",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Miwa, Makoto  and
      Tsujii, Junichi",
    booktitle = "ACL 2025",
    month = aug,
    year = "2025",
    address = "Viena, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.bionlp-1.10/",
    pages = "101--113",
    ISBN = "979-8-89176-275-6",
    abstract = "Extractive question answering over clinical text is a crucial need to help deal with the deluge of clinical text generated in hospitals. While encoder models (e.g., BERT) have been popular for this reading comprehension{--}style question answering task, recently encoder-decoder models (e.g., T5) are on the rise. There is also the emergence of preference optimization techniques to align decoder-only LLMs with human preferences. In this paper, we combine encoder-decoder models with the direct preference optimization (DPO) method for the RadQA radiology question answering task. Our approach achieves a 12{--}15 F1 point improvement over previous state-of-the-art models. To the best of our knowledge, this effort is the first to show that DPO method also works for reading comprehension via novel heuristics to generate preference data without human inputs."
}