  Building natural language inference (NLI) benchmarks that are both
challenging for modern techniques, and free from shortcut biases is difficult.
Chief among these biases is "single sentence label leakage," where
annotator-introduced spurious correlations yield datasets where the logical
relation between (premise, hypothesis) pairs can be accurately predicted from
only a single sentence, something that should in principle be impossible. We
demonstrate that despite efforts to reduce this leakage, it persists in modern
datasets that have been introduced since its 2018 discovery. To enable future
amelioration efforts, introduce a novel model-driven technique, the progressive
evaluation of cluster outliers (PECO) which enables both the objective
measurement of leakage, and the automated detection of subpopulations in the
data which maximally exhibit it.
