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  11. Large Language Models For Transforming Categorical Data To Interpretable Feature Vectors.
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Artificial Intelligence
  9. Natural Language Processing
  11. Large Language Models For Transforming Categorical Data To Interpretable Feature Vectors.

Related Experiment Video

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Large Language Models for Transforming Categorical Data to Interpretable Feature Vectors.

Karim Huesmann, Lars Linsen

    IEEE Transactions on Visualization and Computer Graphics
    |September 30, 2024

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces a new method using Large Language Models (LLMs) to convert categorical data into numerical feature vectors. This facilitates integrated analysis and allows for intuitive user adjustments and enhanced data visualization.

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    Area of Science:

    • Data Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Analyzing heterogeneous data with numerical and categorical attributes often requires separate treatment or transformation of data types.
    • Transforming categorical attributes to numerical ones enables integrated multivariate analysis.

    Purpose of the Study:

    • To propose a novel technique for transforming categorical data into interpretable numerical feature vectors using Large Language Models (LLMs).
    • To facilitate integrated multivariate analysis of heterogeneous datasets.
    • To enable intuitive user adjustment and improve AI-generated outputs through an interactive tool.

    Main Methods:

    • Utilizing Large Language Models (LLMs) to identify key characteristics of categorical attributes.
    • Assigning numerical values to these characteristics to generate multi-dimensional feature vectors.
  • Developing an interactive tool for validating and refining AI-generated transformations.
  • Proposing novel methods for ordering and color-coding categories based on feature vector similarities.

    • Main Results:

    • Successful transformation of categorical data into interpretable numerical feature vectors using LLMs.
    • Demonstration of a fully automated transformation process with options for intuitive user adjustment.
    • Validation of the approach through an interactive tool.
    • Development of new methods for category ordering and color-coding based on generated feature vectors.

    Conclusions:

    • LLMs offer a powerful and interpretable approach to transforming categorical data for integrated analysis.
    • The proposed technique enhances data analysis by providing numerical representations of categorical attributes.
    • The interactive tool empowers users to refine and validate AI-driven data transformations.
    • Novel visualization methods can be derived from the generated feature vectors for improved data exploration.