X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation (2026.findings-acl)
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Kun Zhao, Chenghao Xiao, Sixing Yan, Haoteng Tang, William K. Cheung, Noura Al Moubayed, Liang Zhan, Chenghua Lin
| Challenge: | Technical language and templated nature of professional reports hinder patient comprehension and allow models to artificially boost lexical metrics such as BLEU by reproducing common report patterns. |
| Approach: | They propose a layman's RRG framework that leverages layperson-friendly language to enhance patient accessibility and promote robust evaluation and report generation by encouraging models to focus on semantic accuracy over rigid templates. |
| Outcome: | The proposed framework improves model performance with more layman-style data, compared to templated professional language and inflated lexical scores. |
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