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Parameter-efficient fine-tuning (PEFT) has become a key training strategy for large language models.
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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Introduction to the conll-2003 shared task: Language-independent named entity recognition
Erik F Sang and Fien De Meulder. 2003 · 2003
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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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A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 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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Automated hate speech detection and the problem of offensive language
Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017 · 2017
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Fine-pruning: Defending against backdooring attacks on deep neural networks
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg. 2018 · 2018
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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 Lee, and Kristina Toutanova. 2019 · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
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Weight poisoning attacks on pretrained models
Keita Kurita, Paul Michel, and Graham Neubig. 2020 · 2020
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Adapterhub: A framework for adapting transformers
Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vulić, Sebastian Ruder, Kyunghyun Cho, and Iryna Gurevych. 2020 · 2020
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Backdoor attacks against transfer learning with pre-trained deep learning models
Shuo Wang, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen, and Tianle Chen. 2020 · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
Cited alongside, same era.
Design and evaluation of a multi-domain trojan detection method on deep neural networks
Yansong Gao, Yeonjae Kim, Bao Gia Doan, Zhi Zhang, Gongxuan Zhang, Surya Nepal, Damith C Ranasinghe, and Hyoungshick Kim. 2021 · 2021
Cited alongside, same era.
Towards a unified view of parameter-efficient transfer learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig. 2021 · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2021 · 2021
A gradient control method for backdoor attacks on parameter-efficient tuning
Naibin Gu, Peng Fu, Xiyu Liu, Zhengxiao Liu, Zheng Lin, and Weiping Wang. 2023 · 2023
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Fewer is more: Trojan attacks on parameter-efficient fine-tuning
Lauren Hong and Ting Wang. 2023 · 2023
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Defending against insertion-based textual backdoor attacks via attribution
Jiazhao Li, Zhuofeng Wu, Wei Ping, Chaowei Xiao, and VG Vinod Vydiswaran. 2023 · 2023
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Gpt understands, too
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2023 · 2023
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Defending pre-trained language models as few-shot learners against backdoor attacks
Zhaohan Xi, Tianyu Du, Changjiang Li, Ren Pang, Shouling Ji, Jinghui Chen, Fenglong Ma, and Ting Wang. 2023 · 2023
Later among the works it cites.
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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
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 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.
Adversarial neuron pruning purifies backdoored deep models
Dongxian Wu and Yisen Wang. 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.
Bite: Textual backdoor attacks with iterative trigger injection
Jun Yan, Vansh Gupta, and Xiang Ren. 2023 · 2023
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Language models are super mario: Absorbing abilities from homologous models as a free lunch
Le Yu, Bowen Yu, Haiyang Yu, Fei Huang, and Yongbin Li. 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
Later among the works it cites.
Removing backdoors in pre-trained models by regularized continual pre-training
Biru Zhu, Ganqu Cui, Yangyi Chen, Yujia Qin, Lifan Yuan, Chong Fu, Yangdong Deng, Zhiyuan Liu, Maosong Sun, and Ming Gu. 2023 · 2023
Later among the works it cites.
Claim-guided textual backdoor attack for practical applications
Minkyoo Song, Hanna Kim, Jaehan Kim, Youngjin Jin, and Seungwon Shin. 2024 · 2024
Closest in time.
Lmsanitator: Defending prompt-tuning against task-agnostic backdoors
Chengkun Wei, Wenlong Meng, Zhikun Zhang, Min Chen, Minghu Zhao, Wenjing Fang, Lei Wang, Zihui Zhang, and Wenzhi Chen. 2024 · 2024
Closest in time.
Defending against weight-poisoning backdoor attacks for parameter-efficient fine-tuning
Shuai Zhao, Leilei Gan, Luu Anh Tuan, Jie Fu, Lingjuan Lyu, Meihuizi Jia, and Jinming Wen. 2024 · 2024
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Dppa: Pruning method for large language model to model merging
Yaochen Zhu, Rui Xia, and Jiajun Zhang. 2024 · 2024
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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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