FreeLB: Enhanced Adversarial Training for Natural Language Understanding (1909.11764v5)

Abstract: Adversarial training, which minimizes the maximal risk for label-preserving input perturbations, has proved to be effective for improving the generalization of LLMs. 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 .

• The paper introduces FreeLB, which applies adversarial perturbations to word embeddings to enhance transformer model robustness and generalization.
• The paper reports significant performance gains on benchmarks, boosting BERT-base from 78.3 to 79.4 and RoBERTa-large from 88.5 to 88.8.
• The paper demonstrates that integrating adversarial training with dropout, by reusing dropout masks, can further optimize model performance.

An Analysis of "FreeLB: Enhanced Adversarial Training for Natural Language Understanding"

The paper "FreeLB: Enhanced Adversarial Training for Natural Language Understanding" introduces a novel adversarial training method aimed at enhancing the generalization capabilities of transformer-based LLMs, such as BERT, RoBERTa, and ALBERT. The method, named FreeLB (Free Large-Batch), improves upon traditional adversarial training techniques by incorporating adversarial perturbations directly into the word embeddings, which are then optimized within a constraint-specified region. This approach seeks to address the robustness-generalization trade-off often seen in adversarial training.

Main Contributions

1. Adversarial Training Algorithm: FreeLB distinguishes itself by applying adversarial perturbations to the embedding space, thereby promoting invariance and minimizing adversarial risk across local regions surrounding input samples. This is achieved with a computational cost similar to Projected Gradient Descent (PGD)-based adversarial training methods.
2. Empirical Evaluation: The efficacy of FreeLB is validated through experiments on the GLUE benchmark, showing notable improvements in test scores. Specifically, the method lifts the BERT-base model's overall score from 78.3 to 79.4 and RoBERTa-large's from 88.5 to 88.8. FreeLB also achieves state-of-the-art single-model results for the ARC benchmark.
3. Comparison with Baseline Models: Comparative studies against other adversarial training methodologies such as PGD, FreeAT, and YOPO demonstrate that FreeLB exhibits superior robustness and generalization performance across a range of datasets.
4. Integration with Dropout: The paper explores the interaction between adversarial training and dropout, proposing a version of FreeLB that reuses the same dropout mask across iterations, which leads to improved model performance.

Implications

Practical implications of this research include enhanced robustness for LLMs in NLP applications, making them better suited for real-world tasks prone to adversarial attacks. Theoretical implications suggest that adversarial robustness, when effectively integrated as in FreeLB, can lead to significant improvements in a model’s generalization ability. This provides a new direction for future research in bridging the gap between robustness and generalization.

Future Directions

Future developments could include optimizing FreeLB for reduced computational overhead and adapting the approach to other architectures beyond transformers. Exploring the applicability of FreeLB to more diverse NLP tasks could further expand its impact. Moreover, the methodology could be integrated with novel training strategies to further enhance scalability and efficiency.

Conclusion

FreeLB represents a meaningful advancement in adversarial training, effectively mitigating the generalization degradation often associated with robust training techniques. It successfully leverages adversarial perturbations to improve both robustness and performance on natural language tasks, providing a promising avenue for advancing the current state-of-the-art in LLM training.