Traditional accessibility methods like alternative text and data tables typically underrepresent data visualization's full potential. Keyboard-based chart navigation has emerged as a potential solution, yet efficient data exploration remains challenging. We present VizAbility, a novel system that enriches chart content navigation with conversational interaction, enabling users to use natural language for querying visual data trends. VizAbility adapts to the user's navigation position for improved response accuracy and facilitates verbal command-based chart navigation. Furthermore, it can address queries for contextual information, designed to address the needs of visually impaired users. We designed a large language model (LLM)-based pipeline to address these user queries, leveraging chart data & encoding, user context, and external web knowledge. We conducted both qualitative and quantitative studies to evaluate VizAbility's multimodal approach. We discuss further opportunities based on the results, including enhanced benchmark testing and integration with current visualization tools.

翻译：摘要：传统的替代文本和数据表格等无障碍方法通常难以充分展现数据可视化的全部潜力。尽管基于键盘的图表导航作为一种潜在解决方案已经出现，但高效的数据探索仍具挑战性。我们提出VizAbility——一种通过对话交互丰富图表内容导航的新型系统，使用户能够使用自然语言查询可视化数据趋势。VizAbility能够根据用户的导航位置自适应调整以提升响应准确性，并支持基于语音指令的图表导航。此外，该系统可处理上下文信息查询，专为满足视障用户需求而设计。我们构建了基于大语言模型（LLM）的处理流水线，通过整合图表数据与编码方式、用户上下文及外部网络知识来响应用户查询。通过定性与定量研究相结合的方式，我们评估了VizAbility多模态方法的有效性，并基于研究结果探讨了进一步优化的可能性，包括增强基准测试及与现有可视化工具的集成。