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Prompt Learning has recently gained great popularity in bridging the gap between pretraining tasks and various downstream tasks.
Language models are few-shot learners
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Language models as knowledge bases
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Knowprompt: Knowledge-aware prompt-tuning with synergistic optimization for relation extraction
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Ppt: Pre-trained prompt tuning for few-shot learning
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ERNIE: Enhanced language representation with informative entities
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Making pre-trained language models better few-shot learners
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A knowledge-enhanced pretraining model for commonsense story generation
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BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In ACL 2020
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Automatically identifying words that can serve as labels for few-shot text classification
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Exploiting cloze questions for few shot text classification and natural language inference
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It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
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Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
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P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
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Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021b · 2021
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OpenPrompt: An Open-source Framework for Prompt-learning
Ding Ning, Hu Shengding, Zhao Weilin, Chen Yulin, Liu Zhiyuan, Zheng Hai-Tao, and Sun Maosong. 2021 · 2021
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Learning How to Ask: Querying LMs with Mixtures of Soft Prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
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Exploring Low-dimensional Intrinsic Task Subspace via Prompt Tuning
Yujia Qin, Xiaozhi Wang, Yusheng Su, Yankai Lin, Ning Ding, Zhiyuan Liu, Juanzi Li, Lei Hou, Peng Li, Maosong Sun, et al · 2021
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Multimodal few-shot learning with frozen language models
Maria Tsimpoukelli, Jacob Menick, Serkan Cabi, SM Eslami, Oriol Vinyals, and Felix Hill. 2021 · 2021
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Spot: Better frozen model adaptation through soft prompt transfer
Tu Vu, Brian Lester, Noah Constant, Rami Al-Rfou, and Daniel Cer. 2021 · 2021
Later among the works it cites.