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Prompt tuning is one of the most effective solutions to adapting a fixed pre-trained language model (PLM) for various downstream tasks, especially with only a few input samples.
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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A memory-based approach to anti-spam filtering for mailing lists
Georgios Sakkis, Ion Androutsopoulos, Georgios Paliouras, Vangelis Karkaletsis, Constantine D Spyropoulos, and Panagiotis Stamatopoulos. 2003 · 2003
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Bertweet: A pre-trained language model for english tweets
Dat Quoc Nguyen, Thanh Vu, and Anh Tuan Nguyen. 2020 · 2005
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
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Onion: A simple and effective defense against textual backdoor attacks
Fanchao Qi, Yangyi Chen, Mukai Li, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2020 · 2011
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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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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg. 2017 · 2017
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Large scale crowdsourcing and characterization of twitter abusive behavior
Antigoni Founta, Constantinos Djouvas, Despoina Chatzakou, Ilias Leontiadis, Jeremy Blackburn, Gianluca Stringhini, Athena Vakali, Michael Sirivianos, and Nicolas Kourtellis. 2018 · 2018
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How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Pre-trained models: Past, present and future
Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Yuan Yao, Ao Zhang, Liang Zhang, Wentao Han, Minlie Huang, Qin Jin, Yanyan Lan, Yang Liu, Zhiyuan Liu, Zhiwu Lu, Xipeng Qiu, Ruihua Song, Jie Tang, Ji-Rong Wen, Jinhui Yuan, Wayne Xin Zhao, and Jun Zhu. 2021 · 2021
Earlier work this paper cites.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Cited alongside, same era.
Hidden killer: Invisible textual backdoor attacks with syntactic trigger
Fanchao Qi, Mukai Li, Yangyi Chen, Zhengyan Zhang, Zhiyuan Liu, Yasheng Wang, and Maosong Sun. 2021 · 2021
Cited alongside, same era.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
Cited alongside, same era.
Rap: Robustness-aware perturbations for defending against backdoor attacks on nlp models
Wenkai Yang, Yankai Lin, Peng Li, Jie Zhou, and Xu Sun. 2021 · 2021
Cited alongside, same era.
Badprompt: Backdoor attacks on continuous prompts
Xiangrui Cai, Haidong Xu, Sihan Xu, Ying Zhang, et al. 2022 · 2022
Jiaqi Xue and Qian Lou. 2022 · 2022
Later among the works it cites.
Ontology-enhanced prompt-tuning for few-shot learning
Hongbin Ye, Ningyu Zhang, Shumin Deng, Xiang Chen, Hui Chen, Feiyu Xiong, Xi Chen, and Huajun Chen. 2022 · 2022
Later among the works it cites.
Differentiable prompt makes pre-trained language models better few-shot learners
Ningyu Zhang, Luoqiu Li, Xiang Chen, Shumin Deng, Zhen Bi, Chuanqi Tan, Fei Huang, and Huajun Chen. 2022 · 2022
Later among the works it cites.
Trojbits: A hardware aware inference-time attack on transformer-based language models
Mansour Al Ghanim, Muhammad Santriaji, Qian Lou, and Yan Solihin. 2023 · 2023
Closest in time.
Backdoor learning on sequence to sequence models
Lichang Chen, Minhao Cheng, and Heng Huang. 2023 · 2023
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Cited alongside, same era.
Ppt: Backdoor attacks on pre-trained models via poisoned prompt tuning
Wei Du, Yichun Zhao, Boqun Li, Gongshen Liu, and Shilin Wang. 2022 · 2022
Cited alongside, same era.
PPT: Pre-trained prompt tuning for few-shot learning
Yuxian Gu, Xu Han, Zhiyuan Liu, and Minlie Huang. 2022 · 2022
Cited alongside, same era.
Trojtext: Test-time invisible textual trojan insertion
Qian Lou, Yepeng Liu, and Bo Feng. 2022 · 2022
Cited alongside, same era.
Template-free prompt tuning for few-shot NER
Ruotian Ma, Xin Zhou, Tao Gui, Yiding Tan, Linyang Li, Qi Zhang, and Xuanjing Huang. 2022 · 2022
Cited alongside, same era.
Promptattack: Prompt-based attack for language models via gradient search
Yundi Shi, Piji Li, Changchun Yin, Zhaoyang Han, Lu Zhou, and Zhe Liu. 2022 · 2022
Cited alongside, same era.
Exploring the universal vulnerability of prompt-based learning paradigm
Lei Xu, Yangyi Chen, Ganqu Cui, Hongcheng Gao, and Zhiyuan Liu. 2022 · 2022
Cited alongside, same era.
Trojvit: Trojan insertion in vision transformers
Mengxin Zheng, Qian Lou, and Lei Jiang. 2023a
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Detecting backdoors in pre-trained encoders
Shiwei Feng, Guanhong Tao, Siyuan Cheng, Guangyu Shen, Xiangzhe Xu, Yingqi Liu, Kaiyuan Zhang, Shiqing Ma, and Xiangyu Zhang. 2023 · 2023
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NOTABLE: Transferable backdoor attacks against prompt-based NLP models
Kai Mei, Zheng Li, Zhenting Wang, Yang Zhang, and Shiqing Ma. 2023 · 2023
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Decodingtrust: A comprehensive assessment of trustworthiness in gpt models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, et al. 2023 · 2023
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Trojllm: A black-box trojan prompt attack on large language models
Jiaqi Xue, Mengxin Zheng, Ting Hua, Yilin Shen, Yepeng Liu, Ladislau Bölöni, and Qian Lou. 2024 · 2024
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Badencoder: Backdoor attacks to pre-trained encoders in self-supervised learning
Jinyuan Jia, Yupei Liu, and Neil Zhenqiang Gong. 2022 · 2059
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