IEEE VIS 2024 Content: A Preliminary Roadmap for LLMs as Visual Data Analysis Assistants

A Preliminary Roadmap for LLMs as Visual Data Analysis Assistants

Harry Li - MIT Lincoln Laboratory, Lexington, United States

Gabriel Appleby - Tufts University, Medford, United States

Ashley Suh - MIT Lincoln Laboratory, Lexington, United States

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Room: Bayshore II

2024-10-14T16:00:00ZGMT-0600Change your timezone on the schedule page
2024-10-14T16:00:00Z
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We present a mixed-methods study to explore how large language models (LLMs) can assist users in the visual exploration and analysis of complex data structures, using knowledge graphs (KGs) as a baseline. We surveyed and interviewed 20 professionals who regularly work with LLMs with the goal of using them for (or alongside) KGs. From the analysis of our interviews, we contribute a preliminary roadmap for the design of LLM-driven visual analysis systems and outline future opportunities in this emergent design space.
Abstract

We present a mixed-methods study to explore how large language models (LLMs) can assist users in the visual exploration and analysis of complex data structures, using knowledge graphs (KGs) as a baseline. We surveyed and interviewed 20 professionals who regularly work with LLMs with the goal of using them for (or alongside) KGs. From the analysis of our interviews, we contribute a preliminary roadmap for the design of LLM-driven visual analysis systems and outline future opportunities in this emergent design space.