Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuning

By Chenglei Si, Zhengyan Zhang, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Qun Liu, Maosong Sun. 2020

37 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xu Han
2. Xiao Liu
3. Ning Ding
4. Zhiyuan Liu
5. Minlie Huang
6. Yuxian Gu
7. Xipeng Qiu
8. Zhengyan Zhang

Citations in the corpus

• Pre-Trained Models: Past, Present and Future (2021, background): “Adversarial defenses (Si et al., 2020; Yao et al., 2021; Li and Qiu, 2021) for PTMs are also promising, which can improve the robustness of PTMs and make them immune against adversarial attacks.”
• Prompting GPT-3 To Be Reliable (2022, unknown): “…paraphrase, or distractor insertion (Jin et al., 2020; Li et al., 2020; Ribeiro et al., 2018; Si et al., 2019; 2021a; Jia & Liang, 2017; Si et al., 2021b); LLMs have been shown to exploit shortcuts or spurious correlations in the training data which fail on counter test examples…”
• Data Augmentation Approaches in Natural Language Processing: A Survey (2021, methodology): “Self-training [33], [85], [63], [84] [34], [130] [85] Mixup [35], [89], [91], [131] [36] [92]”