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、用户友好型技术的需求日益增长，网页无障碍性对于确保视障、听障、认知障碍或行动障碍等残障人士能够平等访问在线内容至关重要。尽管已有《网页内容无障碍指南》（WCAG）和《无障碍网页倡议》（W3C）等无障碍标准与指南，但超过90%的网站仍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。虽然已有研究提出了定位和识别无障碍错误的方法，但尚无研究聚焦于如何有效修正此类违规问题。本文提出了一种创新方法，通过利用基础模型实时修改文档对象模型（DOM）来修正网页中的无障碍违规行为。借助无障碍错误信息、大语言模型（LLMs）及提示工程技术，我们在自建基准测试集ACCESS上实现了超过51%的无障碍违规错误率降低。本研究为构建包容性网络内容提供了有价值的实现路径，并为探索自动化网页无障碍的先进方法指明了未来研究方向。