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The prompt-based learning paradigm, which bridges the gap between pre-training and fine-tuning, achieves state-of-the-art performance on several NLP tasks, particularly in few-shot settings.
Language models are few-shot learners
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Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
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Visualizing data using t-sne
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Recursive deep models for semantic compositionality over a sentiment treebank
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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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Spectral signatures in backdoor attacks
Brandon Tran, Jerry Li, and Aleksander Madry. 2018 · 2018
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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 Bowman. 2018 · 2018
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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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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 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, et al. 2019 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
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Predicting the type and target of offensive posts in social media
Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, et al. 2019 · 2019
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, et al. 2019 · 2019
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Towards robustness against natural language word substitutions
Xinshuai Dong, Anh Tuan Luu, Rongrong Ji, and Hong Liu. 2020 · 2020
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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al. 2020 · 2020
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Weight poisoning attacks on pretrained models
Keita Kurita, Paul Michel, and Graham Neubig. 2020 · 2020
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Improving adversarial robustness requires revisiting misclassified examples
Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, et al. 2020 · 2020
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Word-level textual adversarial attacking as combinatorial optimization
Yuan Zang, Fanchao Qi, Chenghao Yang, Zhiyuan Liu, Meng Zhang, Qun Liu, and Maosong Sun. 2020 · 2020
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Clean-label backdoor attacks on video recognition models
Shihao Zhao, Xingjun Ma, Xiang Zheng, James Bailey, et al. 2020 · 2020
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Badprompt: Backdoor attacks on continuous prompts
Xiangrui Cai, Haidong Xu, Sihan Xu, Ying Zhang, et al. 2022 · 2022
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Kallima: A clean-label framework for textual backdoor attacks
Xiaoyi Chen, Yinpeng Dong, Zeyu Sun, Shengfang Zhai, Qingni Shen, and Zhonghai Wu. 2022 · 2022
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Ppt: Backdoor attacks on pre-trained models via poisoned prompt tuning
Wei Du, Yichun Zhao, Boqun Li, Gongshen Liu, and Shilin Wang. 2022 · 2022
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Triggerless backdoor attack for nlp tasks with clean labels
Leilei Gan, Jiwei Li, Tianwei Zhang, Xiaoya Li, Yuxian Meng, Fei Wu, et al. 2022 · 2022
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Badhash: Invisible backdoor attacks against deep hashing with clean label
Shengshan Hu, Ziqi Zhou, Yechao Zhang, Leo Yu Zhang, Yifeng Zheng, Yuanyuan He, and Hai Jin. 2022 · 2022
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Backdoors against natural language processing: A review
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Fewshotqa: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models
Rakesh Chada and Pradeep Natarajan. 2021 · 2021
Cited alongside, same era.
Mitigating backdoor attacks in lstm-based text classification systems by backdoor keyword identification
Chuanshuai Chen and Jiazhu Dai. 2021 · 2021
Cited alongside, same era.
Badnl: Backdoor attacks against nlp models
Xiaoyi Chen, Ahmed Salem, Michael Backes, Shiqing Ma, and Yang Zhang. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 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, and Ruotian Ma. 2021 · 2021
Cited alongside, same era.
Shaofeng Li, Tian Dong, Benjamin Zi Hao Zhao, Minhui Xue, et al. 2022 · 2022
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Cins: Comprehensive instruction for few-shot learning in task-oriented dialog systems
Fei Mi, Yasheng Wang, and Yitong Li. 2022 · 2022
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Noisy channel language model prompting for few-shot text classification
Sewon Min, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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Textual manifold-based defense against natural language adversarial examples
Dang Nguyen Minh and Anh Tuan Luu. 2022 · 2022
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Improving neural cross-lingual abstractive summarization via employing optimal transport distance for knowledge distillation
Thong Thanh Nguyen and Anh Tuan Luu. 2022 · 2022
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The power of prompt tuning for low-resource semantic parsing
Nathan Schucher, Siva Reddy, and Harm de Vries. 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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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 · 2023
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From softmax to nucleusmax: A novel sparse language model for chinese radiology report summarization
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Backdoor attacks with input-unique triggers in nlp
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