Papers with adversarial training approaches
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder (2021.findings-acl)
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| Challenge: | Recent work has shown that models can be easily fooled by intentionally designed adversarial examples. |
| Approach: | They propose two efficient approaches for generating adversarial perturbations on embeddings and propose two new approaches to help model learn adversarials more efficiently. |
| Outcome: | The proposed approaches outperform strong baselines on various text classification datasets and the model's performance drops less under adversarial attack. |
Attention-Focused Adversarial Training for Robust Temporal Reasoning (2022.lrec-1)
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| Challenge: | Current adversarial training approaches for NLP add adversarials to the embedding layer, ignoring other layers. |
| Approach: | They propose an enhanced adversarial training algorithm for fine-tuning transformer-based language models . they add the adversarials to multiple hidden states or attention representations of the model layers . |
| Outcome: | The proposed model improves performance on several temporal reasoning benchmarks and establishes new state-of-the-art results. |
ORTicket: Let One Robust BERT Ticket Transfer across Different Tasks (2024.lrec-main)
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| Challenge: | Pretrained language models are susceptible to subtle perturbations and require multiple adversarial training during fine-tuning to improve their robustness. |
| Approach: | They propose a novel adversarial defense method ORTicket that fine-tunes a model for downstream tasks. |
| Outcome: | The proposed method achieves comparable robustness to other defense methods while maintaining the efficiency of fine-tuning. |