  Data modification, either via additional training datasets, data
augmentation, debiasing, and dataset filtering, has been proposed as an
effective solution for generalizing to out-of-domain (OOD) inputs, in both
natural language processing and computer vision literature. However, the effect
of data modification on adversarial robustness remains unclear. In this work,
we conduct a comprehensive study of common data modification strategies and
evaluate not only their in-domain and OOD performance, but also their
adversarial robustness (AR). We also present results on a two-dimensional
synthetic dataset to visualize the effect of each method on the training
distribution. This work serves as an empirical study towards understanding the
relationship between generalizing to unseen domains and defending against
adversarial perturbations. Our findings suggest that more data (either via
additional datasets or data augmentation) benefits both OOD accuracy and AR.
However, data filtering (previously shown to improve OOD accuracy on natural
language inference) hurts OOD accuracy on other tasks such as question
answering and image classification. We provide insights from our experiments to
inform future work in this direction.
