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Even though the large-scale language models have achieved excellent performances, they suffer from various adversarial attacks.
Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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The fifth pascal recognizing textual entailment challenge
Luisa Bentivogli, Peter Clark, Ido Dagan, and Danilo Giampiccolo. 2009 · 2009
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013 · 2013
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
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Qualitatively characterizing neural network optimization problems
Ian J. Goodfellow and Oriol Vinyals. 2015 · 2015
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Shakeout: A new regularized deep neural network training scheme
Guoliang Kang, Jun Li, and Dacheng Tao. 2016 · 2016
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Squad: 100, 000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Entropy-sgd: Biasing gradient descent into wide valleys
Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer T. Chayes, Levent Sagun, and Riccardo Zecchina. 2017 · 2017
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Quora question pairs
Zihan Chen, Hongbo Zhang, Xiaoji Zhang, and Leqi Zhao. 2018 · 2018
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Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein. 2018a · 2018
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Deep text classification can be fooled
Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, and Wenchang Shi. 2018 · 2018
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Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell. 2018 · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019 · 2019
Cited alongside, same era.
PAWS: Paraphrase adversaries from word scrambling
Yuan Zhang, Jason Baldridge, and Luheng He. 2019 · 2019
Cited alongside, same era.
Recall and learn: Fine-tuning deep pretrained language models with less forgetting
Sanyuan Chen, Yutai Hou, Yiming Cui, Wanxiang Che, Ting Liu, and Xiangzhan Yu. 2020 · 2020
Cited alongside, same era.
From hero to zéroe: A benchmark of low-level adversarial attacks
Steffen Eger and Yannik Benz. 2020 · 2020
Cited alongside, same era.
Evaluating models’ local decision boundaries via contrast sets
Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, and Ben Zhou. 2020 · 2020
Cited alongside, same era.
Evaluating and enhancing the robustness of neural network-based dependency parsing models with adversarial examples
Xiaoqing Zheng, Jiehang Zeng, Yi Zhou, Cho-Jui Hsieh, Minhao Cheng, and Xuanjing Huang. 2020 · 2020
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Freelb: Enhanced adversarial training for natural language understanding
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, and Jingjing Liu. 2020 · 2020
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How should pre-trained language models be fine-tuned towards adversarial robustness?
Xinshuai Dong, Anh Tuan Luu, Min Lin, Shuicheng Yan, and Hanwang Zhang. 2021 · 2021
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SimCSE: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
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Distance-based regularisation of deep networks for fine-tuning
Henry Gouk, Timothy M. Hospedales, and Massimiliano Pontil. 2021 · 2021
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SMART: robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization
Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Tuo Zhao. 2020 · 2020
Cited alongside, same era.
Is bert really robust? a strong baseline for natural language attack on text classification and entailment
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2020 · 2020
Cited alongside, same era.
Mixout: Effective regularization to finetune large-scale pretrained language models
Cheolhyoung Lee, Kyunghyun Cho, and Wanmo Kang. 2020 · 2020
Cited alongside, same era.
Adversarial NLI: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020 · 2020
Cited alongside, same era.
Beyond accuracy: Behavioral testing of NLP models with CheckList
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2020
Cited alongside, same era.
An investigation of why overparameterization exacerbates spurious correlations
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, and Percy Liang. 2020 · 2020
Cited alongside, same era.
Learning which features matter: RoBERTa acquires a preference for linguistic generalizations (eventually)
Alex Warstadt, Yian Zhang, Xiaocheng Li, Haokun Liu, and Samuel R. Bowman. 2020 · 2020
Cited alongside, same era.
Achieving model robustness through discrete adversarial training
Maor Ivgi and Jonathan Berant. 2021 · 2021
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R-drop: Regularized dropout for neural networks
Xiaobo Liang, Lijun Wu, Juntao Li, Yue Wang, Qi Meng, Tao Qin, Wei Chen, Min Zhang, and Tie-Yan Liu. 2021 · 2021
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Using adversarial attacks to reveal the statistical bias in machine reading comprehension models
Jieyu Lin, Jiajie Zou, and Nai Ding. 2021 · 2021
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Better robustness by more coverage: Adversarial and mixup data augmentation for robust finetuning
Chenglei Si, Zhengyan Zhang, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Qun Liu, and Maosong Sun. 2021 · 2021
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Raise a child in large language model: Towards effective and generalizable fine-tuning
Runxin Xu, Fuli Luo, Zhiyuan Zhang, Chuanqi Tan, Baobao Chang, Songfang Huang, and Fei Huang. 2021 · 2021
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Revisiting few-sample BERT fine-tuning
Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q. Weinberger, and Yoav Artzi. 2021 · 2021
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On length divergence bias in textual matching models
Lan Jiang, Tianshu Lyu, Yankai Lin, Meng Chong, Xiaoyong Lyu, and Dawei Yin. 2022 · 2022
Closest in time.
SHIELD: Defending textual neural networks against multiple black-box adversarial attacks with stochastic multi-expert patcher
Thai Le, Noseong Park, and Dongwon Lee. 2022 · 2022
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