[30] Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al. Matching networks for one shot learning. Advances in neural information processing systems, 29, 2016.
[31] Wei Wang, Vincent W Zheng, Han Yu, and Chunyan Miao. A survey of zero-shot learning: Settings, methods, and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1-37, 2019.
[32] Yaqing Wang, Quanming Yao, James T Kwok, and Lionel M Ni. Generalizing from a Few Examples: A Survey on Few-shot Learning. ACM computing surveys (csur), 53(3):1-34, 2020.
[33] Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le. Unsupervised Data Augmentation for Consistency Training. In Proceedings of Advances in Neural Information Processing Systems (NeurIPS), 2020.
[34] Lei Xu, Yangyi Chen, Ganqu Cui, Hongcheng Gao, and Zhiyuan Liu. Exploring the Universal Vulnerability of Prompt-based Learning Paradigm. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL), 2022.
[35] Wenkai Yang, Lei Li, Zhiyuan Zhang, Xuancheng Ren, Xu Sun, and Bin He. Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models. In Proceedings of Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2021.
[36] Wenkai Yang, Yankai Lin, Peng Li, Jie Zhou, and Xu Sun. RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021.
[37] Wenkai Yang, Yankai Lin, Peng Li, Jie Zhou, and Xu Sun. Rethinking Stealthiness of Backdoor Attack against NLP Models. In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL), 2021.
[38] Wenpeng Yin, Nazneen Fatema Rajani, Dragomir Radev, Richard Socher, and Caiming Xiong. Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.
[39] Haiyang Yu, Ningyu Zhang, Shumin Deng, Hongbin Ye, Wei Zhang, and Huajun Chen. Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction. In Proceedings of International Conference on Computational Linguistics, 2020.
[40] Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, and Bowen Zhou. Diverse Few-Shot Text Classification with Multiple Metrics. In Proceedings of Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2018.
[41] Ningyu Zhang, Luoqiu Li, Xiang Chen, Shumin Deng, Zhen Bi, Chuanqi Tan, Fei Huang, and Huajun Chen. Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners. In Proceedings of International Conference on Learning Representations (ICLR), 2021.
[42] Xinyang Zhang, Zheng Zhang, Shouling Ji, and Ting Wang. Trojaning language models for fun and profit. In Proceedings of IEEE European Symposium on Security and Privacy (Euro S&P), 2021.
[43] Zhiyuan Zhang, Xuancheng Ren, Qi Su, Xu Sun, and Bin He. Neural Network Surgery: Injecting Data Patterns into Pre-trained Models with Minimal Instance-wise Side Effects. In Proceedings of Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2021.
[44] Zexuan Zhong, Dan Friedman, and Danqi Chen. Factual Probing Is [MASK]: Learning vs. Learning to Recall. In Proceedings of Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2021.