IEEE VIS 2024 Content: LEVA: Using Large Language Models to Enhance Visual Analytics

LEVA: Using Large Language Models to Enhance Visual Analytics

Yuheng Zhao -

Yixing Zhang -

Yu Zhang -

Xinyi Zhao -

Junjie Wang -

Zekai Shao -

Cagatay Turkay -

Siming Chen -

Screen-reader Accessible PDF
Download preprint PDF
DOI: 10.1109/TVCG.2024.3368060

Room: Bayshore I

2024-10-16T16:24:00ZGMT-0600Change your timezone on the schedule page
2024-10-16T16:24:00Z
Exemplar figure, described by caption below
LEVA is a framework that uses large language models to enhance users' VA workflows at multiple stages: onboarding, exploration, and summarization. An implementation of LEVA comprises four components: (A) Users can communicate with LLMs and control the insight annotations in the Chat view; (B) The recommended insights for the next step of analysis from LLMs are updated in the Original system view; (C) Users can retrace the interaction history in the Interaction stream view; (D) Once a historical analysis path is selected, the generated insight report will display in the Report view.
Fast forward
Full Video
Keywords

Insight recommendation, mixed-initiative, interface agent, large language models, visual analytics

Abstract

Visual analytics supports data analysis tasks within complex domain problems. However, due to the richness of data types, visual designs, and interaction designs, users need to recall and process a significant amount of information when they visually analyze data. These challenges emphasize the need for more intelligent visual analytics methods. Large language models have demonstrated the ability to interpret various forms of textual data, offering the potential to facilitate intelligent support for visual analytics. We propose LEVA, a framework that uses large language models to enhance users' VA workflows at multiple stages: onboarding, exploration, and summarization. To support onboarding, we use large language models to interpret visualization designs and view relationships based on system specifications. For exploration, we use large language models to recommend insights based on the analysis of system status and data to facilitate mixed-initiative exploration. For summarization, we present a selective reporting strategy to retrace analysis history through a stream visualization and generate insight reports with the help of large language models. We demonstrate how LEVA can be integrated into existing visual analytics systems. Two usage scenarios and a user study suggest that LEVA effectively aids users in conducting visual analytics.