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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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Designing for Collaboration: Visualization to Enable Human-LLM Analytical Partnership.

Mai Elshehaly, Radu Jianu, Aidan Slingsby

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    Summary
    This summary is machine-generated.

    Dynamic visualizations are crucial for effective human-large language model (LLM) collaboration in data analysis. Visualizing evolving analytical artifacts and provenance enhances transparency and insight in LLM-assisted workflows.

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    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Artificial Intelligence

    Background:

    • Visualization artifacts traditionally support human collaboration and knowledge transfer in data analysis.
    • The role of visualization in capturing knowledge during human-large language model (LLM) interaction remains underexplored.
    • Current LLM workflows in analytics are often linear and text-based, hindering structured representation of the analytical process.

    Purpose of the Study:

    • To investigate the potential of visualization artifacts in human-LLM analytical workflows.
    • To highlight the limitations of current LLM text-based approaches for tracking and structuring analysis.
    • To advocate for dynamic visual representations to enhance human-LLM collaboration and knowledge externalization.

    Main Methods:

    • Exploration of current opportunities and limitations of LLMs in tracking, structuring, and visualizing analytic processes.
    • Conceptual argument for the integration of dynamic visualization in human-LLM workflows.
    • Proposal of a research agenda informed by LLM advancements.

    Main Results:

    • LLMs' linear text-based workflows limit the traceability and structure of analytical artifacts.
    • Dynamic visual representations are proposed as critical for structuring evolving artifacts and provenance.
    • Opportunities and limitations for using LLMs to visualize analytic processes are demonstrated.

    Conclusions:

    • Dynamic visualization is essential for effective human-LLM analytical interactions.
    • Visualizing evolving artifacts and provenance can lead to more structured and transparent analytical processes.
    • Further research is needed to leverage LLM capabilities for enhanced visualization in analytics.