  Adversarial training, which minimizes the maximal risk for label-preserving
input perturbations, has proved to be effective for improving the
generalization of language models. In this work, we propose a novel adversarial
training algorithm, FreeLB, that promotes higher invariance in the embedding
space, by adding adversarial perturbations to word embeddings and minimizing
the resultant adversarial risk inside different regions around input samples.
To validate the effectiveness of the proposed approach, we apply it to
Transformer-based models for natural language understanding and commonsense
reasoning tasks. Experiments on the GLUE benchmark show that when applied only
to the finetuning stage, it is able to improve the overall test scores of
BERT-base model from 78.3 to 79.4, and RoBERTa-large model from 88.5 to 88.8.
In addition, the proposed approach achieves state-of-the-art single-model test
accuracies of 85.44\% and 67.75\% on ARC-Easy and ARC-Challenge. Experiments on
CommonsenseQA benchmark further demonstrate that FreeLB can be generalized and
boost the performance of RoBERTa-large model on other tasks as well. Code is
available at \url{https://github.com/zhuchen03/FreeLB .
