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Building natural language inference (NLI) benchmarks that are bothchallenging for modern techniques, and free from shortcut biases is difficult.Chief among these biases is "single sentence label leakage," whereannotator-introduced spurious correlations yield datasets where the logicalrelation between (premise, hypothesis) pairs can be accurately predicted fromonly a single sentence, something that should in principle be impossible. Wedemonstrate that despite efforts to reduce this leakage, it persists in moderndatasets that have been introduced since its 2018 discovery. To enable futureamelioration efforts, introduce a novel model-driven technique, the progressiveevaluation of cluster outliers (PECO) which enables both the objectivemeasurement of leakage, and the automated detection of subpopulations in thedata which maximally exhibit it.

PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outliers