With the increasing need for inclusive and user-friendly technology, web accessibility is crucial to ensuring equal access to online content for individuals with disabilities, including visual, auditory, cognitive, or motor impairments. Despite the existence of accessibility guidelines and standards such as Web Content Accessibility Guidelines (WCAG) and the Web Accessibility Initiative (W3C), over 90\% of websites still fail to meet the necessary accessibility requirements. For web users with disabilities, there exists a need for a tool to automatically fix web page accessibility errors. While research has demonstrated methods to find and target accessibility errors, no research has focused on effectively correcting such violations. This paper presents a novel approach to correcting accessibility violations on the web by modifying the document object model (DOM) in real time with foundation models. Leveraging accessibility error information, large language models (LLMs), and prompt engineering techniques, we achieved greater than a 51\% reduction in accessibility violation errors after corrections on our novel benchmark: ACCESS. Our work demonstrates a valuable approach toward the direction of inclusive web content, and provides directions for future research to explore advanced methods to automate web accessibility.

翻译：随着对包容性和用户友好型技术的需求日益增长，网络无障碍对于确保残障人士（包括视觉、听觉、认知或运动障碍者）平等访问在线内容至关重要。尽管存在《网络内容无障碍指南》（WCAG）和《网络无障碍倡议》（W3C）等无障碍指导原则与标准，但仍有超过90%的网站未能满足必要的无障碍要求。对于残障网络用户而言，亟需一种能够自动修复网页无障碍错误的工具。虽然已有研究展示了发现和定位无障碍错误的方法，但尚无研究专注于有效修正此类违规行为。本文提出了一种新颖方法，通过利用基础模型实时修改文档对象模型（DOM）来修正网络上的无障碍违规。借助无障碍错误信息、大语言模型（LLM）以及提示工程技术，我们在自制基准测试ACCESS上实现了超过51%的无障碍违规错误减少。本工作为朝向包容性网络内容方向提供了一种有价值的途径，并为未来探索自动化网络无障碍的先进方法指明了研究方向。