Papers with *discriminator*

    1 papers
    Predicting generalization performance with correctness discriminators (2024.findings-emnlp)

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    Challenge: Existing models estimate accuracy of models on unlabeled test data, but they hide their own uncertainty.
    Approach: They propose a model that establishes upper and lower bounds on the accuracy without requiring gold labels for the unseen data.
    Outcome: The proposed model establishes upper and lower bounds on accuracy without requiring gold labels for the unseen data.

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