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Pre-trained language models (PLMs) have attracted enormous attention over the past few years with their unparalleled performances.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Bad characters: Imperceptible nlp attacks
Nicholas Boucher, Ilia Shumailov, Ross Anderson, and Nicolas Papernot. 2022 · 2004
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016 · 2016
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg. 2017 · 2017
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Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang. 2018 · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018 · 2018
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Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein. 2018 · 2018
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Boolq: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019 · 2019
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A backdoor attack against lstm-based text classification systems
Jiazhu Dai, Chuanshuai Chen, and Yufeng Li. 2019 · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019 · 2019
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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 · 2019
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2019 · 2019
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Overfitting mechanism and avoidance in deep neural networks
Shaeke Salman and Xiuwen Liu. 2019 · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao. 2019 · 2019
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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. 2019 · 2019
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Trojai https://pages.nist.gov/trojai/
IARPA. 2020 · 2020
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Hidden trigger backdoor attacks
Aniruddha Saha, Akshayvarun Subramanya, and Hamed Pirsiavash. 2020 · 2020
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AutoPrompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
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Backdoor embedding in convolutional neural network models via invisible perturbation
Haoti Zhong, Cong Liao, Anna Cinzia Squicciarini, Sencun Zhu, and David Miller. 2020 · 2020
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Trojaning language models for fun and profit
Xinyang Zhang, Zheng Zhang, Shouling Ji, and Ting Wang. 2021a · 2021
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Trojaning language models for fun and profit
Xinyang Zhang, Zheng Zhang, Shouling Ji, and Ting Wang. 2021b · 2021
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One-shot neural backdoor erasing via adversarial weight masking
Shuwen Chai and Jinghui Chen. 2022 · 2022
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022 · 2022
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A unified evaluation of textual backdoor learning: Frameworks and benchmarks
Ganqu Cui, Lifan Yuan, Bingxiang He, Yangyi Chen, Zhiyuan Liu, and Maosong Sun. 2022 · 2022
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Ahmad Rijal Azizi, Ibrahim Asadullah Tahmid, Asim Waheed, Neal Mangaokar, Jiameng Pu, Mobin Javed, Chandan K. Reddy, and Bimal Viswanath. 2021 · 2021
Cited alongside, same era.
Badnl: Backdoor attacks against nlp models with semantic-preserving improvements
Xiaoyi Chen, Ahmed Salem, Dingfan Chen, Michael Backes, Shiqing Ma, Qingni Shen, Zhonghai Wu, and Yang Zhang. 2021 · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Cited alongside, same era.
Backdoor attacks on pre-trained models by layerwise weight poisoning
Linyang Li, Demin Song, Xiaonan Li, Jiehang Zeng, Ruotian Ma, and Xipeng Qiu. 2021 · 2021
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P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2021 · 2021
Cited alongside, same era.
ONION: A simple and effective defense against textual backdoor attacks
Fanchao Qi, Yangyi Chen, Mukai Li, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2021a · 2021
Cited alongside, same era.
Backdoor pre-trained models can transfer to all
Lujia Shen, Shouling Ji, Xuhong Zhang, Jinfeng Li, Jing Chen, Jie Shi, Chengfang Fang, Jianwei Yin, and Ting Wang. 2021 · 2021
Cited alongside, same era.
Hidden trigger backdoor attack on NLP models via linguistic style manipulation
Xudong Pan, Mi Zhang, Beina Sheng, Jiaming Zhu, and Min Yang. 2022 · 2022
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Constrained optimization with dynamic bound-scaling for effective NLP backdoor defense
Guangyu Shen, Yingqi Liu, Guanhong Tao, Qiuling Xu, Zhuo Zhang, Shengwei An, Shiqing Ma, and Xiangyu Zhang. 2022 · 2022
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Exploring the universal vulnerability of prompt-based learning paradigm
Lei Xu, Yangyi Chen, Ganqu Cui, Hongcheng Gao, and Zhiyuan Liu. 2022 · 2022
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A prompt pattern catalog to enhance prompt engineering with chatgpt
Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, and Douglas C. Schmidt. 2023 · 2023
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Instructions as backdoors: Backdoor vulnerabilities of instruction tuning for large language models
Jiashu Xu, Mingyu Derek Ma, Fei Wang, Chaowei Xiao, and Muhao Chen. 2023 · 2023
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Red alarm for pre-trained models: Universal vulnerability to neuron-level backdoor attacks
Zhengyan Zhang, Guangxuan Xiao, Yongwei Li, Tian Lv, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Xin Jiang, and Maosong Sun. 2023 · 2023
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Prompt as triggers for backdoor attack: Examining the vulnerability in language models
Shuai Zhao, Jinming Wen, Luu Anh Tuan, Junbo Zhao, and Jie Fu. 2023 · 2023
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Piccolo: Exposing complex backdoors in nlp transformer models
Yingqi Liu, Guangyu Shen, Guanhong Tao, Shengwei An, Shiqing Ma, and Xiangyu Zhang. 2022b · 2042
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Be careful about poisoned word embeddings: Exploring the vulnerability of the embedding layers in NLP models
Wenkai Yang, Lei Li, Zhiyuan Zhang, Xuancheng Ren, Xu Sun, and Bin He. 2021a · 2058
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