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Related Experiment Video

Updated: Jul 1, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Published on: December 6, 2024

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LEVA: Using Large Language Models to Enhance Visual Analytics.

Yuheng Zhao, Yixing Zhang, Yu Zhang

    IEEE Transactions on Visualization and Computer Graphics
    |March 4, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Large language models (LLMs) enhance visual analytics (VA) by assisting users with onboarding, exploration, and summarization. LEVA, a new framework, uses LLMs to streamline complex data analysis workflows.

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    Area of Science:

    • Computer Science
    • Human-Computer Interaction

    Background:

    • Visual analytics (VA) is crucial for complex data analysis but demands significant user cognitive load due to diverse data types and interactions.
    • Existing VA methods require improvement in intelligent support to manage information processing challenges.

    Purpose of the Study:

    • To introduce LEVA, a framework leveraging large language models (LLMs) to enhance user workflows in visual analytics.
    • To improve user efficiency and effectiveness across multiple stages of the VA process: onboarding, exploration, and summarization.

    Main Methods:

    • LEVA utilizes LLMs to interpret visualization designs and relationships for user onboarding.
    • LLMs recommend insights based on system status and data analysis to facilitate mixed-initiative exploration.
    • A selective reporting strategy combined with LLMs generates insight reports by retracing analysis history.

    Main Results:

    • LEVA can be integrated into existing visual analytics systems.
    • Demonstrated effectiveness through two usage scenarios and a user study.
    • LEVA significantly aids users in conducting visual analytics tasks.

    Conclusions:

    • Large language models offer a powerful approach to developing more intelligent visual analytics systems.
    • The LEVA framework effectively supports users throughout the visual analytics lifecycle.
    • Future work can explore further integration of LLMs to advance human-AI collaboration in data analysis.