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Prompt-tuning has emerged as an attractive paradigm for deploying large-scale language models due to its strong downstream task performance and efficient multitask serving ability.
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Spam filtering with naive bayes-which naive bayes?
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
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Pointer sentinel mixture models
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Learn&fuzz: Machine learning for input fuzzing
P. Godefroid, H. Peleg, and R. Singh · 2017
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
T. Gu, B. Dolan-Gavitt, and S. Garg · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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Detecting backdoor attacks on deep neural networks by activation clustering
B. Chen, W. Carvalho, N. Baracaldo, H. Ludwig, B. Edwards, T. Lee, I. Molloy, and B. Srivastava · 2018
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Iotfuzzer: Discovering memory corruptions in iot through app-based fuzzing
J. Chen, W. Diao, Q. Zhao, C. Zuo, Z. Lin, X. Wang, W. C. Lau, M. Sun, R. Yang, and K. Zhang · 2018
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Multi-step retriever-reader interaction for scalable open-domain question answering
R. Das, S. Dhuliawala, M. Zaheer, and A. McCallum · 2018
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Visualizing the loss landscape of neural nets
H. Li, Z. Xu, G. Taylor, C. Studer, and T. Goldstein · 2018
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Fine-pruning: Defending against backdooring attacks on deep neural networks
K. Liu, B. Dolan-Gavitt, and S. Garg · 2018
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Trojaning attack on neural networks
Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, and X. Zhang · 2018
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Spectral signatures in backdoor attacks
B. Tran, J. Li, and A. Mądry · 2018
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BoolQ: Exploring the surprising difficulty of natural yes/no questions
C. Clark, K. Lee, M.-W. Chang, T. Kwiatkowski, M. Collins, and K. Toutanova · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
J. Devlin, M. Chang, K. Lee, and K. Toutanova · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
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Robust anomaly detection and backdoor attack detection via differential privacy
M. Du, R. Jia, and D. Song · 2019
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Abs: Scanning neural networks for back-doors by artificial brain stimulation
Y. Liu, W.-C. Lee, G. Tao, S. Ma, Y. Aafer, and X. Zhang · 2019
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
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Language models as knowledge bases?
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Defending neural backdoors via generative distribution modeling
X. Qiao, Y. Yang, and H. Li · 2019
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Aspect-level sentiment analysis via convolution over dependency tree
K. Sun, R. Zhang, S. Mensah, Y. Mao, and X. Liu · 2019
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Universal adversarial triggers for attacking and analyzing NLP
E. Wallace, S. Feng, N. Kandpal, M. Gardner, and S. Singh · 2019
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Glue: A multi-task benchmark and analysis platform for natural language understanding
A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Deberta: Decoding-enhanced bert with disentangled attention
P. He, X. Liu, J. Gao, and W. Chen · 2021
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How can we know when language models know? on the calibration of language models for question answering
Z. Jiang, J. Araki, H. Ding, and G. Neubig · 2021
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The power of scale for parameter-efficient prompt tuning
B. Lester, R. Al-Rfou, and N. Constant · 2021
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Backdoor attacks on pre-trained models by layerwise weight poisoning
L. Li, D. Song, X. Li, J. Zeng, R. Ma, and X. Qiu · 2021
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Hidden backdoors in human-centric language models
S. Li, H. Liu, T. Dong, B. Z. H. Zhao, M. Xue, H. Zhu, and J. Lu · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
X. L. Li and P. Liang · 2021
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Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le · 2019
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ERNIE: Enhanced language representation with informative entities
Z. Zhang, X. Han, Z. Liu, X. Jiang, M. Sun, and Q. Liu · 2019
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Effectively pretraining a speech translation decoder with machine translation data
A. Alinejad and A. Sarkar · 2020
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Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
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Aspect sentiment classification with document-level sentiment preference modeling
X. Chen, C. Sun, J. Wang, S. Li, L. Si, M. Zhang, and G. Zhou · 2020
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Weight poisoning attacks on pretrained models
K. Kurita, P. Michel, and G. Neubig · 2020
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ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut · 2020
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Neural attention distillation: Erasing backdoor triggers from deep neural networks
Y. Li, X. Lyu, N. Koren, L. Lyu, B. Li, and X. Ma · 2021
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P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
X. Liu, K. Ji, Y. Fu, Z. Du, Z. Yang, and J. Tang · 2021
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X. Liu, Y. Zheng, Z. Du, M. Ding, Y. Qian, Z. Yang, and J. Tang · 2021
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ONION: A simple and effective defense against textual backdoor attacks
F. Qi, Y. Chen, M. Li, Y. Yao, Z. Liu, and M. Sun · 2021
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Learning how to ask: Querying LMs with mixtures of soft prompts
G. Qin and J. Eisner · 2021
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It’s not just size that matters: Small language models are also few-shot learners
T. Schick and H. Schütze · 2021
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Backdoor pre-trained models can transfer to all
L. Shen, S. Ji, X. Zhang, J. Li, J. Chen, J. Shi, C. Fang, J. Yin, and T. Wang · 2021
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Demon in the variant: Statistical analysis of { \{ DNNs } \} for robust backdoor contamination detection
D. Tang, X. Wang, H. Tang, and K. Zhang · 2021
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Multi-head highly parallelized lstm decoder for neural machine translation
H. Xu, Q. Liu, J. van Genabith, D. Xiong, and M. Zhang · 2021
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RAP: Robustness-Aware Perturbations for defending against backdoor attacks on NLP models
W. Yang, Y. Lin, P. Li, J. Zhou, and X. Sun · 2021
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Rethinking stealthiness of backdoor attack against NLP models
W. Yang, Y. Lin, P. Li, J. Zhou, and X. Sun · 2021
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Trojaning language models for fun and profit
X. Zhang, Z. Zhang, S. Ji, and T. Wang · 2021
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Red alarm for pre-trained models: Universal vulnerability to neuron-level backdoor attacks
Z. Zhang, G. Xiao, Y. Li, T. Lv, F. Qi, Z. Liu, Y. Wang, X. Jiang, and M. Sun · 2021
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Spinning language models: Risks of propaganda-as-a-service and countermeasures
E. Bagdasaryan and V. Shmatikov · 2022
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BadPrompt: Backdoor Attacks on Continuous Prompts
X. Cai, H. Xu, S. Xu, Y. ZHANG, and Y. xiaojie · 2022
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Ppt: Backdoor attacks on pre-trained models via poisoned prompt tuning
W. Du, Y. Zhao, B. Li, G. Liu, and S. Wang · 2022
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Piccolo: Exposing complex backdoors in nlp transformer models
Y. Liu, G. Shen, G. Tao, S. An, S. Ma, and X. Zhang · 2022
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Constrained optimization with dynamic bound-scaling for effective NLP backdoor defense
G. Shen, Y. Liu, G. Tao, Q. Xu, Z. Zhang, S. An, S. Ma, and X. Zhang · 2022
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Better trigger inversion optimization in backdoor scanning
G. Tao, G. Shen, Y. Liu, S. An, Q. Xu, S. Ma, P. Li, and X. Zhang · 2022
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Exploring the universal vulnerability of prompt-based learning paradigm
L. Xu, Y. Chen, G. Cui, H. Gao, and Z. Liu · 2022
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Stanford alpaca: An instruction-following llama model
R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto · 2023
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