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Recent work has indicated that many natural language understanding andreasoning datasets contain statistical cues that may be taken advantaged of byNLP models whose capability may thus be grossly overestimated. To discover thepotential weakness in the models, some human-designed stress tests have beenproposed but they are expensive to create and do not generalize to arbitrarymodels. We propose a light-weight and general statistical profiling framework,ICQ (I-See-Cue), which automatically identifies possible biases in anymultiple-choice NLU datasets without the need to create any additional testcases, and further evaluates through blackbox testing the extent to whichmodels may exploit these biases.

Statistically Profiling Biases in Natural Language Reasoning Datasets and Models