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GenNI: Human-AI Collaboration for Data-Backed Text Generation.

Hendrik Strobelt, Jambay Kinley, Robert Krueger

    IEEE Transactions on Visualization and Computer Graphics
    |September 29, 2021
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    Summary

    GenNI is a visual system for human-AI collaboration in text generation from data. It offers fine-grained control over machine learning models, preventing misleading outputs for better virtual assistant interfaces.

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    Area of Science:

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

    Background:

    • Machine learning-based Table2Text systems are crucial for natural language interfaces.
    • Uncontrolled generation by these systems can lead to misleading or unexpected outputs.

    Purpose of the Study:

    • To introduce GenNI (Generation Negotiation Interface), an interactive visual system for human-AI collaboration in descriptive text generation.
    • To enable users to globally constrain deep learning model generations without losing representational power.

    Main Methods:

    • Developed a deep learning model with explicit control states.
    • Implemented a visual interface for a Refine-Forecast interaction paradigm.
    • Integrated user control mechanisms into the generation process.

    Main Results:

    • GenNI improves upon uncontrolled generation approaches in producing descriptive text.
    • The system provides fine-grained control over the text generation process.
    • Demonstrated effectiveness through multiple use cases and two experiments.

    Conclusions:

    • GenNI facilitates effective human-AI collaboration for reliable text generation.
    • The system enhances the suitability of AI-generated text for user needs.
    • Offers a novel approach to controlling complex machine learning models in practical applications.