  Existing works have shown that fine-tuned textual transformer models achieve
state-of-the-art prediction performances but are also vulnerable to adversarial
text perturbations. Traditional adversarial evaluation is often done
\textit{only after} fine-tuning the models and ignoring the training data. In
this paper, we want to prove that there is also a strong correlation between
training data and model robustness. To this end, we extract 13 different
features representing a wide range of input fine-tuning corpora properties and
use them to predict the adversarial robustness of the fine-tuned models.
Focusing mostly on encoder-only transformer models BERT and RoBERTa with
additional results for BART, ELECTRA, and GPT2, we provide diverse evidence to
support our argument. First, empirical analyses show that (a) extracted
features can be used with a lightweight classifier such as Random Forest to
predict the attack success rate effectively, and (b) features with the most
influence on the model robustness have a clear correlation with the robustness.
Second, our framework can be used as a fast and effective additional tool for
robustness evaluation since it (a) saves 30x-193x runtime compared to the
traditional technique, (b) is transferable across models, (c) can be used under
adversarial training, and (d) robust to statistical randomness. Our code is
publicly available at \url{https://github.com/CaptainCuong/RobustText_ACL2024}.
