  Fine-tuning large-scale pre-trained language models has been demonstrated
effective for various natural language processing (NLP) tasks. Previous studies
have established that incorporating adversarial training during the fine-tuning
stage can significantly enhance model generalization and robustness. However,
from the perspective of game theory, such utilizations of adversarial training
correspond to pure-strategy games, which are inherently limited in terms of the
scope of their strategies, thereby still having room for improvement. In order
to push the performance boundaries, we propose a novel Mixed-strategy
Adversarial Training algorithm (MAT). Methodologically, we derive the Nash
equilibrium of a mixed-strategy game for adversarial training using Entropy
Mirror Descent to establish MAT by sampling method. To verify the effectiveness
of MAT, we conducted extensive benchmark experiments on large-scale pre-trained
models, such as BERT and RoBERTa. MAT significantly outperforms the
state-of-the-art methods on both the GLUE and ANLI benchmarks in terms of
generalization and robustness.
