A11yn: Aligning LLMs for Web Accessibility-Aware UI Generation
Janghan Yoon · Jaegwan Cho · Junhyeok Kim · Jiwan Chung · Jaehyun Jeon · Seungwon Lim · Youngjae Yu
We introduce A11yn, the first method for aligning code-generating LLMs to produce web accessibility-aware web UIs.
摘要
Large language models can generate visually coherent web UIs from natural language requests, but they frequently violate Web Content Accessibility Guidelines (WCAG), excluding users with diverse needs and contexts. We address this by introducing A11yn, a post-training framework for web accessibility-aware web UI generation. A11yn converts off-the-shelf WCAG audits into a verifiable web accessibility reward, enabling accessibility alignment without data from dense human annotations. To support training and evaluation, we release UIReq-6.8K, a dataset of 6,800 diverse UI generation instructions, and RealUIReq-300, a benchmark of 300 realistic web UI generation tasks. Experimental results show that A11yn achieves the strongest web accessibility compliance among baselines, reducing the inaccessibility rate by 87.5\% and 58.1\% under two independent WCAG auditors relative to the base model. Unlike prompting-based post-hoc accessibility-correction baselines, A11yn achieves these gains in a single-pass generation setting while maintaining comparable semantic fidelity and visual quality. The code and dataset are available at \url{https://github.com/jeffrobot/A11yn}.