  Contrast consistency, the ability of a model to make consistently correct
predictions in the presence of perturbations, is an essential aspect in NLP.
While studied in tasks such as sentiment analysis and reading comprehension, it
remains unexplored in open-domain question answering (OpenQA) due to the
difficulty of collecting perturbed questions that satisfy factuality
requirements. In this work, we collect minimally edited questions as
challenging contrast sets to evaluate OpenQA models. Our collection approach
combines both human annotation and large language model generation. We find
that the widely used dense passage retriever (DPR) performs poorly on our
contrast sets, despite fitting the training set well and performing
competitively on standard test sets. To address this issue, we introduce a
simple and effective query-side contrastive loss with the aid of data
augmentation to improve DPR training. Our experiments on the contrast sets
demonstrate that DPR's contrast consistency is improved without sacrificing its
accuracy on the standard test sets.
