Computer Science Faculty Publications
Document Type
Conference Proceeding
Publication Date
7-2026
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.
Recommended Citation
Zhao, Kun, Chenghao Xiao, Sixing Yan, Haoteng Tang, William K. Cheung, Noura Al Moubayed, Liang Zhan, and Chenghua Lin. "X-ray made simple: Lay radiology report generation and robust evaluation." In Findings of the Association for Computational Linguistics: ACL 2026, pp. 34583-34598. 2026. https://doi.org/10.18653/v1/2026.findings-acl.1726
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
Findings of the Association for Computational Linguistics: ACL 2026
DOI
10.18653/v1/2026.findings-acl.1726
Comments
©2026 Association for Computational Linguistics. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.