@inproceedings{zhao-etal-2026-x,
    title = "{X}-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation",
    author = "Zhao, Kun  and
      Xiao, Chenghao  and
      Yan, Sixing  and
      Tang, Haoteng  and
      Cheung, William K.  and
      Al Moubayed, Noura  and
      Zhan, Liang  and
      Lin, Chenghua",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1726/",
    pages = "34583--34598",
    ISBN = "979-8-89176-395-1",
    abstract = "While multimodal generative models have advanced radiology report generation (RRG), challenges remain in making reports accessible to patients and ensuring reliable evaluation. The technical language and templated nature of professional reports hinder patient comprehension and enable models to artificially boost lexical metrics such as BLEU by reproducing common report patterns. To address these limitations, we propose the Layman{'}s RRG framework, which leverages layperson-friendly language to enhance patient accessibility and promote more robust evaluation and report generation by encouraging models to focus on semantic accuracy over rigid templates. Our approach also introduces and releases two refined layman-style datasets (at the sentence and report levels), along with a semantics-based evaluation metric that mitigates inflated lexical scores and a layman-guided training strategy. Experiments show that training on layman-style data helps models better capture the meaning of clinical findings. Notably, we observe a positive scaling law: model performance improves with more layman-style data, in contrast to the inverse trend observed with templated professional language."
}