Papers with Data Generation

    1 papers
    Targeted Data Generation: Finding and Fixing Model Weaknesses (2023.acl-long)

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    Challenge: Existing models fail systematically on specific subgroups of data, resulting in unfair outcomes and eroding user trust.
    Approach: They propose a framework that automatically identifies challenging subgroups and generates new data for those subgroup using large language models with a human in the loop.
    Outcome: The proposed framework improves accuracy on challenging subgroups while improving overall test accuracy.

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