用AI连接自然语言与可视化，让生物医学数据探索更直观高效。
YAC: Bridging Natural Language and Interactive Visual Exploration with Generative AI for Biomedical Data Discovery
- 通过多智能体工具调用生成结构化指令，驱动交互式图表与筛选。
- 专家用户研究发现系统在意图理解与反馈提示方面仍有优化空间。
- 适合生物医学研究人员快速探索复杂数据，尤其擅长处理非结构化查询。
将自然语言输入融入生物医学数据发现界面具有巨大潜力。然而，用户界面元素和可视化仍是与数据交互的重要工具。在我们的原型系统YAC（Yet Another Chatbot）中，我们融合了自然语言与交互式可视化。YAC采用工具调用的多智能体系统生成声明式输出，该输出被解析以渲染联动的交互式可视化并应用数据过滤。我们还引入可调整控件，允许用户直接修改结构化输出。同时生成结构化文本以澄清用户意图、告知系统边界，并通过实时数据链接解释数据细节。我们通过领域专家用户研究揭示了YAC可改进之处，并对其技术维度进行了深入分析。
原文摘要 · Abstract (English)
Incorporating natural language input has the potential to improve the capabilities of biomedical data discovery interfaces. However, user interface elements and visualizations are still powerful tools for interacting with data. In our prototype system, YAC, Yet Another Chatbot, we integrate natural language and interactive visualizations. YAC uses a tool-calling multi-agent system to generate declarative output, which is interpreted to render linked interactive visualizations and apply data filters. We also include adjustment widgets, which allow users to directly modify the structured output. Structured text is also generated to clarify user intent, notify users of system boundaries, and explain aspects of the data with live data element links. We conducted a user study with domain experts to surface areas where YAC can be improved. Furthermore we reflect on the capabilities and design of this system with an analysis of its technical dimensions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。