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While the Large Language Models (LLMs) dominate a majority of languageunderstanding tasks, previous work shows that some of these results aresupported by modelling spurious correlations of training datasets. Authorscommonly assess model robustness by evaluating their models onout-of-distribution (OOD) datasets of the same task, but these datasets mightshare the bias of the training dataset.  We propose a simple method for measuring a scale of models' reliance on anyidentified spurious feature and assess the robustness towards a large set ofknown and newly found prediction biases for various pre-trained models anddebiasing methods in Question Answering (QA). We find that the reported OODgains of debiasing methods can not be explained by mitigated reliance on biasedfeatures, suggesting that biases are shared among QA datasets. We furtherevidence this by measuring that performance of OOD models depends on biasfeatures comparably to the ID model, motivating future work to refine thereports of LLMs' robustness to a level of known spurious features.

Think Twice: Measuring the Efficiency of Eliminating Prediction Shortcuts of Question Answering Models