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    Large language models (LLMs) enhance data analysis but complicate insight management. InsightLens, a new system, streamlines recording and navigation of insights, improving efficiency for data analysts.

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    Area of Science:

    • Artificial Intelligence
    • Human-Computer Interaction
    • Data Science

    Background:

    • Large language models (LLMs) have advanced natural language interfaces (NLIs) for data analysis, enabling complex reasoning.
    • Current chat-based LLM interfaces struggle with managing insights entangled with code, visualizations, and explanations, hindering efficient workflow.

    Purpose of the Study:

    • To understand data analysts' workflows and pain points in managing insights from LLM-powered data analysis.
    • To introduce and evaluate InsightLens, a system designed to automate insight recording and organization, and facilitate navigation.

    Main Methods:

    • A formative study was conducted with eight data analysts to identify challenges in insight management.
    • An LLM-agent-based framework, InsightLens, was developed to automate insight recording and organization.
    • InsightLens visualizes conversational contexts to aid insight navigation, and its effectiveness was evaluated through a user study with twelve data analysts.

    Main Results:

    • InsightLens automates the recording and organization of data analysis insights within LLM conversations.
    • The system effectively visualizes complex conversational contexts, improving insight navigation.
    • User studies demonstrated that InsightLens significantly reduces manual and cognitive effort for data analysts.

    Conclusions:

    • InsightLens enhances the efficiency of LLM-powered data analysis by addressing insight management challenges.
    • The system integrates seamlessly into existing conversational analysis workflows without disruption.
    • InsightLens offers a promising solution for improving the overall data analysis experience with LLMs.