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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Interactive Table Synthesis With Natural Language.

Yanwei Huang, Yunfan Zhou, Ran Chen

    IEEE Transactions on Visualization and Computer Graphics
    |November 1, 2023
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    Summary
    This summary is machine-generated.

    This study introduces NL2Rigel, a tool using natural language (NL) for easier table creation. It significantly reduces the time needed for data transformation tasks compared to existing methods.

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    Area of Science:

    • Data Science
    • Human-Computer Interaction

    Background:

    • Data transformation into tabular views is complex and time-consuming.
    • Existing interactive tools often require users to have significant data transformation knowledge.

    Purpose of the Study:

    • To improve accessibility for average users in data transformation and table generation.
    • To develop an interactive tool leveraging natural language (NL) for intuitive table synthesis and editing.

    Main Methods:

    • Developed NL2Rigel, an interactive tool for table synthesis from semi-structured text using NL instructions.
    • Utilized a large language model and prompting techniques to interpret NL instructions into a table synthesis pipeline (Rigel specifications).
    • Designed an interface for visualizing the pipeline and generated tables, enabling refinement via targeted NL instructions.

    Main Results:

    • NL2Rigel successfully interprets NL instructions into interpretable data transformation pipelines.
    • The tool facilitates intuitive table generation and incremental refinement through NL interaction.
    • A comparative user study showed significantly shorter task completion times with NL2Rigel compared to the original Rigel, with comparable completion rates.

    Conclusions:

    • NL2Rigel lowers the barrier for average users to perform complex data transformations and generate tables.
    • The NL-driven approach enhances usability and efficiency in tabular data synthesis and editing.
    • NL2Rigel demonstrates a promising direction for accessible data wrangling tools.