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Prompting method is regarded as one of the crucial progress for few-shot nature language processing.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019 · 1910
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Newsweeder: Learning to filter netnews
Ken Lang. 1995 · 1995
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Reuters-21578 text categorization test collection
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Rcv1: A new benchmark collection for text categorization research
David D Lewis, Yiming Yang, Tony Russell-Rose, and Fan Li. 2004 · 2004
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Alex Graves, Greg Wayne, and Ivo Danihelka. 2014 · 2014
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Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, Ruslan Salakhutdinov, et al. 2015 · 2015
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Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, and Nando de Freitas. 2016 · 2016
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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
Ruining He and Julian McAuley. 2016 · 2016
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Fasttext. zip: Compressing text classification models
Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Hérve Jégou, and Tomas Mikolov. 2016 · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al. 2016 · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
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Meta-sgd: Learning to learn quickly for few-shot learning
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li. 2017 · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and H. Larochelle. 2017 · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel. 2017 · 2017
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Fewrel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2018 · 2018
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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel. 2018 · 2018
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News category dataset, 06
Rishabh Misra. 2018 · 2018
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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman. 2018 · 2018
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Few-shot image recognition by predicting parameters from activations
Siyuan Qiao, Chenxi Liu, Wei Shen, and Alan L Yuille. 2018 · 2018
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How to train your maml
Antreas Antoniou, Harri Edwards, and Amos Storkey. 2019 · 2019
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BERTese: Learning to speak to BERT
Adi Haviv, Jonathan Berant, and Amir Globerson. 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. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Don’t miss the labels: Label-semantic augmented meta-learner for few-shot text classification
Qiaoyang Luo, Lingqiao Liu, Yuhao Lin, and Wei Zhang. 2021 · 2021
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Sculpting Data for ML: The first act of Machine Learning
Rishabh Misra and Jigyasa Grover. 2021 · 2021
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Few-shot text classification with distributional signatures
Yujia Bao, Menghua Wu, Shiyu Chang, and Regina Barzilay. 2019 · 2019
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Meta-learning with differentiable closed-form solvers
L Bertinetto, J Henriques, PHS Torr, and A Vedaldi. 2019 · 2019
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A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang. 2019 · 2019
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Commonsense knowledge mining from pretrained models
Joe Davison, Joshua Feldman, and Alexander M Rush. 2019 · 2019
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Multi-level matching and aggregation network for few-shot relation classification
Zhi-Xiu Ye and Zhen-Hua Ling. 2019 · 2019
Cited alongside, same era.
How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2020 · 2020
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Distinct label representations for few-shot text classification
Sora Ohashi, Junya Takayama, Tomoyuki Kajiwara, and Yuki Arase. 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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Exploiting cloze-questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2021 · 2021
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2021 · 2021
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Frog-gnn: Multi-perspective aggregation based graph neural network for few-shot text classification
Shiyao Xu and Yang Xiang. 2021 · 2021
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Knowledge-aware meta-learning for low-resource text classification
Huaxiu Yao, Ying-xin Wu, Maruan Al-Shedivat, and Eric Xing. 2021 · 2021
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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Contrastnet: A contrastive learning framework for few-shot text classification
Junfan Chen, Richong Zhang, Yongyi Mao, and Jie Xue. 2022 · 2022
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Prototypical verbalizer for prompt-based few-shot tuning
Ganqu Cui, Shengding Hu, Ning Ding, Longtao Huang, and Zhiyuan Liu. 2022 · 2022
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