Describe a technique to improve the robustness of a Transformer model to adversarial attacks.

Adversarial training is a technique used to improve the robustness of a Transformer model to adversarial attacks, which are small, carefully crafted perturbations to the input that can cause the model to make incorrect predictions. The basic idea behind adversarial training is to train the model on both clean examples and adversarial examples. This forces the model to learn to be more robust to small changes in the input. To generate adversarial examples, you can use techniques like Fast Gradient Sign Method (FGSM) or Projected Gradient D....