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We build four new test sets for the Stanford Question Answering Dataset(SQuAD) and evaluate the ability of question-answering systems to generalize tonew data. Our first test set is from the original Wikipedia domain and measuresthe extent to which existing systems overfit the original test set. Despiteseveral years of heavy test set re-use, we find no evidence of adaptiveoverfitting. The remaining three test sets are constructed from New York Timesarticles, Reddit posts, and Amazon product reviews and measure robustness tonatural distribution shifts. Across a broad range of models, we observe averageperformance drops of 3.8, 14.0, and 17.4 F1 points, respectively. In contrast,a strong human baseline matches or exceeds the performance of SQuAD models onthe original domain and exhibits little to no drop in new domains. Takentogether, our results confirm the surprising resilience of the holdout methodand emphasize the need to move towards evaluation metrics that incorporaterobustness to natural distribution shifts.

The Effect of Natural Distribution Shift on Question Answering Models