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Large language models have shown their ability to become effective few-shot learners with prompting, revolutionizing the paradigm of learning with data scarcity.
Language models are few-shot learners
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Recursive deep models for semantic compositionality over a sentiment treebank
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Automatically identifying fake news in popular twitter threads
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Decoupled weight decay regularization
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Language models as knowledge bases?
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Language models are unsupervised multitask learners
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Superglue: A stickier benchmark for general-purpose language understanding systems
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Supervised contrastive learning for pre-trained language model fine-tuning
Beliz Gunel, Jingfei Du, Alexis Conneau, and Veselin Stoyanov. 2020 · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2020 · 2020
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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
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Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media
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Pada: A prompt-based autoregressive approach for adaptation to unseen domains
Eyal Ben-David, Nadav Oved, and Roi Reichart. 2021 · 2021
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Flex: Unifying evaluation for few-shot nlp
Jonathan Bragg, Arman Cohan, Kyle Lo, and Iz Beltagy. 2021 · 2021
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Meal: Stable and active learning for few-shot prompting
Abdullatif Köksal, Timo Schick, and Hinrich Schütze. 2022 · 2022
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Cutting down on prompts and parameters: Simple few-shot learning with language models
Robert Logan IV, Ivana Balažević, Eric Wallace, Fabio Petroni, Sameer Singh, and Sebastian Riedel. 2022 · 2022
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Electra is a zero-shot learner, too
Shiwen Ni and Hung-Yu Kao. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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Ontology-enhanced prompt-tuning for few-shot learning
Hongbin Ye, Ningyu Zhang, Shumin Deng, Xiang Chen, Hui Chen, Feiyu Xiong, Xi Chen, and Huajun Chen. 2022 · 2022
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
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How can we know when language models know? on the calibration of language models for question answering
Zhengbao Jiang, Jun Araki, Haibo Ding, and Graham Neubig. 2021 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021b · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Lam Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2021 · 2021
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True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 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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Spt: Learning to selectively insert prompts for better prompt tuning
Wei Zhu and Ming Tan. 2023 · 2022
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Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. 2023 · 2023
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Smop: Towards efficient and effective prompt tuning with sparse mixture-of-prompts
Joon-Young Choi, Junho Kim, Jun-Hyung Park, Wing-Lam Mok, and SangKeun Lee. 2023 · 2023
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Eˆ 2vpt: An effective and efficient approach for visual prompt tuning
Cheng Han, Qifan Wang, Yiming Cui, Zhiwen Cao, Wenguan Wang, Siyuan Qi, and Dongfang Liu. 2023 · 2023
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Dialogue for prompting: a policy-gradient-based discrete prompt optimization for few-shot learning
Chengzhengxu Li, Xiaoming Liu, Yichen Wang, Duyi Li, Yu Lan, and Chao Shen. 2023 · 2023
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OpenAI. 2023 · 2023
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Residual prompt tuning: Improving prompt tuning with residual reparameterization
Anastasia Razdaibiedina, Yuning Mao, Rui Hou, Madian Khabsa, Mike Lewis, Jimmy Ba, and Amjad Almahairi. 2023 · 2023
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Llama: Open and efficient foundation language models
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Making pre-trained language models end-to-end few-shot learners with contrastive prompt tuning
Ziyun Xu, Chengyu Wang, Minghui Qiu, Fuli Luo, Runxin Xu, Songfang Huang, and Jun Huang. 2023 · 2023
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Cold-start data selection for better few-shot language model fine-tuning: A prompt-based uncertainty propagation approach
Yue Yu, Rongzhi Zhang, Ran Xu, Jieyu Zhang, Jiaming Shen, and Chao Zhang. 2023 · 2023
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Infoprompt: Information-theoretic soft prompt tuning for natural language understanding
Junda Wu, Tong Yu, Rui Wang, Zhao Song, Ruiyi Zhang, Handong Zhao, Chaochao Lu, Shuai Li, and Ricardo Henao. 2024 · 2024
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