YAC: Bridging Natural Language and Interactive Visual Exploration with Generative AI for Biomedical Data Discovery
This work addresses the problem of enhancing data exploration interfaces for biomedical researchers by combining generative AI with traditional visualization tools, though it appears incremental in its approach.
The paper tackles the challenge of integrating natural language input with interactive visualizations in biomedical data discovery by developing YAC, a prototype system that uses a multi-agent approach to generate structured output and render linked visualizations, as demonstrated through four usage scenarios.
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, even in the new world of generative AI. In our prototype system, YAC, Yet Another Chatbot, we bridge the gap between natural language and interactive visualizations by generating structured declarative output with a multi-agent system and interpreting that output to render linked interactive visualizations and apply data filters. Furthermore, we include widgets, which allow users to adjust the values of that structured output through user interface elements. We reflect on the capabilities and design of this system with an analysis of its technical dimensions and illustrate the capabilities through four usage scenarios.