  Existing works show that augmenting the training data of pre-trained language
models (PLMs) for classification tasks fine-tuned via parameter-efficient
fine-tuning methods (PEFT) using both clean and adversarial examples can
enhance their robustness under adversarial attacks. However, this adversarial
training paradigm often leads to performance degradation on clean inputs and
requires frequent re-training on the entire data to account for new, unknown
attacks. To overcome these challenges while still harnessing the benefits of
adversarial training and the efficiency of PEFT, this work proposes a novel
approach, called AdpMixup, that combines two paradigms: (1) fine-tuning through
adapters and (2) adversarial augmentation via mixup to dynamically leverage
existing knowledge from a set of pre-known attacks for robust inference.
Intuitively, AdpMixup fine-tunes PLMs with multiple adapters with both clean
and pre-known adversarial examples and intelligently mixes them up in different
ratios during prediction. Our experiments show AdpMixup achieves the best
trade-off between training efficiency and robustness under both pre-known and
unknown attacks, compared to existing baselines on five downstream tasks across
six varied black-box attacks and 2 PLMs. All source code will be available.
