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Updated: Aug 3, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Visual Explanation for Open-Domain Question Answering With BERT.

Zekai Shao, Shuran Sun, Yuheng Zhao

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    VEQA is a visual analytics system designed to improve open-domain question answering (OpenQA) models. It helps experts understand complex model decisions and identify areas for enhancement in natural language processing.

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    Area of Science:

    • Natural Language Processing
    • Artificial Intelligence
    • Human-Computer Interaction

    Background:

    • Open-domain question answering (OpenQA) is crucial for NLP, but faces challenges with complex data, models, and decision processes.
    • Transformer models have advanced OpenQA, yet interpretability remains a significant hurdle for further improvement.

    Purpose of the Study:

    • To introduce VEQA, a visual analytics system for understanding OpenQA model decision-making.
    • To provide insights for enhancing OpenQA model performance and interpretability.

    Main Methods:

    • VEQA visualizes data flow within and between OpenQA modules at summary, instance, and candidate levels.
    • The system employs summary visualizations, ranking visualizations with context, and comparative tree visualizations for detailed analysis.

    Main Results:

    • VEQA facilitates a deeper understanding of the decision process in OpenQA models.
    • The system effectively aids experts in identifying pathways for model improvement and enhancing interpretability.

    Conclusions:

    • VEQA demonstrates significant effectiveness in promoting interpretability for complex OpenQA systems.
    • The visual analytics approach offers valuable insights for advancing machine reading comprehension and NLP research.