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 context 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 improved benchmark testing, incorporation of vision models, and integration with visualization workflows.

翻译：摘要：传统的可访问性方法（如替代文本和数据表格）通常无法充分展现数据可视化的全部潜力。基于键盘的图表导航虽已作为潜在解决方案出现，但高效的数据探索仍然面临挑战。我们提出VizAbility——一种通过对话交互增强图表内容导航的新颖系统，使用户能够以自然语言查询可视化数据趋势。该系统可适应用户的导航上下文以提升响应准确性，并支持基于语音命令的图表导航。此外，它还能处理针对上下文信息的查询，旨在满足视障用户的需求。我们设计了一条基于大语言模型（LLM）的流水线来处理这些用户查询，利用图表数据与编码、用户上下文及外部网络知识。我们通过定性与定量研究评估了VizAbility的多模态方案。基于研究结果，我们进一步探讨了改进基准测试、整合视觉模型以及与可视化工作流集成等未来方向。