Correcting test set contamination by spiking the training data
The core proposal is to spike the training data by intentionally contaminating some test examples at known rates, which can then be used to calibrate predictors of model memorization which enable principled statistical correction of inflated test scores.
J. Wei, Jerry Li, Ameya Godbole et al.
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