  In the era of widespread public use of AI systems across various domains,
ensuring adversarial robustness has become increasingly vital to maintain
safety and prevent undesirable errors. Researchers have curated various
adversarial datasets (through perturbations) for capturing model deficiencies
that cannot be revealed in standard benchmark datasets. However, little is
known about how these adversarial examples differ from the original data
points, and there is still no methodology to measure the intended and
unintended consequences of those adversarial transformations. In this research,
we conducted a systematic survey of existing quantifiable metrics that describe
text instances in NLP tasks, among dimensions of difficulty, diversity, and
disagreement. We selected several current adversarial effect datasets and
compared the distributions between the original and their adversarial
counterparts. The results provide valuable insights into what makes these
datasets more challenging from a metrics perspective and whether they align
with underlying assumptions.
