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Few-shot NLP research is highly active, yet conducted in disjoint research threads with evaluation suites that lack challenging-yet-realistic testing setups and fail to employ careful experimental design.
Release strategies and the social impacts of language models
Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, Ariel Herbert-Voss, Jeff Wu, Alec Radford, and Jasmine Wang. 2019 · 1908
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Efficiently simulating the coverage properties of interval estimates
D. B. Rubin and N. Schenker. 1986 · 1986
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NewsWeeder: Learning to Filter Netnews
Ken Lang. 1995 · 1995
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Reuters-21578 text categorization test collection, distribution 1.0
David D. Lewis. 1997 · 1997
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Building a question answering test collection
Ellen M. Voorhees and Dawn M. Tice. 2000 · 2000
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Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition
Erik F. Tjong Kim Sang and Fien De Meulder. 2003 · 2003
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Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
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A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
Bo Pang and Lillian Lee. 2004 · 2004
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The PASCAL recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
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Automatically Constructing a Corpus of Sentential Paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
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Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales
Bo Pang and Lillian Lee. 2005 · 2005
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The second PASCAL recognising textual entailment challenge
Roy Bar-Haim, Ido Dagan, Bill Dolan, L. Ferro, Danilo Giampiccolo, and B. Magnini. 2006 · 2006
Earlier work this paper cites.
The Third PASCAL Recognizing Textual Entailment Challenge
Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007 · 2007
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Meta-learning for few-shot natural language processing: A survey
Wenpeng Yin. 2020 · 2007
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The fifth PASCAL recognizing textual entailment challenge
Luisa Bentivogli, Peter Clark, Ido Dagan, and Danilo Giampiccolo. 2009 · 2009
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FewJoint: A few-shot learning benchmark for joint language understanding
Yutai Hou, Jiafeng Mao, Yongkui Lai, Cheng Chen, Wanxiang Che, Zhigang Chen, and Ting Liu. 2020 · 2009
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The Winograd schema challenge
Hector Levesque, Ernest Davis, and Leora Morgenstern. 2011 · 2011
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Extracting training data from large language models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel. 2020 · 2012
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Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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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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SQuAD: 100,000+ Questions for Machine Comprehension of Text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, and Daan Wierstra. 2016 · 2016
Earlier work this paper cites.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel. 2017 · 2017
Earlier work this paper cites.
A systematic study of the class imbalance problem in convolutional neural networks
Mateusz Buda, Atsuto Maki, and Maciej A. Mazurowski. 2018 · 2018
Earlier work this paper cites.
The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing
Rotem Dror, Gili Baumer, Segev Shlomov, and Roi Reichart. 2018 · 2018
Cited alongside, same era.
Meta-Learning for Low-Resource Neural Machine Translation
Jiatao Gu, Yong Wang, Yun Chen, Victor O. K. Li, and Kyunghyun Cho. 2018 · 2018
Cited alongside, same era.
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
Cited alongside, same era.
SciTaiL: A Textual Entailment Dataset from Science Question Answering
Tushar Khot, Ashish Sabharwal, and Peter Clark. 2018 · 2018
Cited alongside, same era.
News category dataset
Rishabh Misra. 2018 · 2018
Cited alongside, same era.
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
UnifiedQA: Crossing Format Boundaries With a Single QA System
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, P. Clark, and Hannaneh Hajishirzi. 2020 · 2020
Later among the works it cites.
Learning to classify intents and slot labels given a handful of examples
Jason Krone, Yi Zhang, and Mona Diab. 2020 · 2020
Later among the works it cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, W. Li, and Peter J. Liu. 2020 · 2020
Later among the works it cites.
Green AI
Roy Schwartz, Jesse Dodge, Noah A. Smith, and Oren Etzioni. 2020 · 2020
Later among the works it cites.
Meta-Learning for Few-Shot NMT Adaptation
Amr Sharaf, Hany Hassan, and Hal Daumé III. 2020 · 2020
Later among the works it cites.
AutoPrompt: Eliciting knowledge from language models with automatically generated prompts
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Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2018 · 2018
Cited alongside, same era.
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
Diverse Few-Shot Text Classification with Multiple Metrics
Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, and Bowen Zhou. 2018 · 2018
Cited alongside, same era.
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
Cited alongside, same era.
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Torchmeta: A Meta-Learning library for PyTorch
Tristan Deleu, Tobias Würfl, Mandana Samiei, Joseph Paul Cohen, and Yoshua Bengio. 2019 · 2019
Cited alongside, same era.
Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks
Zi-Yi Dou, Keyi Yu, and Antonios Anastasopoulos. 2019 · 2019
Cited alongside, same era.
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
Later among the works it cites.
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle. 2020 · 2020
Later among the works it cites.
Generalizing from a Few Examples: A Survey on Few-shot Learning
Yaqing Wang, Quanming Yao, James T. Kwok, and Lionel M. Ni. 2020 · 2020
Later among the works it cites.
Learning from Task Descriptions
Orion Weller, Nicholas Lourie, Matt Gardner, and Matthew Peters. 2020 · 2020
Later among the works it cites.
RAFT: A real-world few-shot text classification benchmark
Neel Alex, Eli Lifland, Lewis Tunstall, Abhishek Thakur, Pegah Maham, C. Jess Riedel, Emmie Hine, Carolyn Ashurst, Paul Sedille, Alexis Carlier, Michael Noetel, and Andreas Stuhlmüller. 2021 · 2021
Closest in time.
On the dangers of stochastic parrots: Can language models be too big?
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
Closest in time.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
Closest in time.
Peter Hase and Mohit Bansal. 2021 · 2021
Closest in time.
Cutting down on prompts and parameters: Simple few-shot learning with language models
Robert L. Logan IV, Ivana Balazevic, Eric Wallace, Fabio Petroni, Sameer Singh, and Sebastian Riedel. 2021 · 2021
Closest in time.
Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Wei hua Hu, Michihiro Yasunaga, Richard L. Phillips, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang. 2021 · 2021
Closest in time.
huggingface/datasets: 1.9.0
Quentin Lhoest, Patrick von Platen, Thomas Wolf, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Suraj Patil, Lewis Tunstall, Mariama Drame, Julien Chaumond, Julien Plu, Joe Davison, Simon Brandeis, Victor Sanh, Teven Le Scao, Kevin Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, and François Lagunas. 2021 · 2021
Closest in time.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2021 · 2021
Closest in time.
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
Closest in time.
Few-Shot Learning with Class Imbalance
Mateusz Ochal, Massimiliano Patacchiola, Amos Storkey, Jose Vazquez, and Sen Wang. 2021 · 2021
Closest in time.
True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021 · 2021
Closest in time.
Improving and simplifying pattern exploiting training
Derek Tam, Rakesh R. Menon, Mohit Bansal, Shashank Srivastava, and Colin Raffel. 2021 · 2021
Closest in time.
CrossFit: A few-shot learning challenge for cross-task generalization in NLP
Qinyuan Ye, Bill Yuchen Lin, and Xiang Ren. 2021 · 2021
Closest in time.
Calibrate before use: Improving few-shot performance of language models
Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
Closest in time.
FewNLU: Benchmarking state-of-the-art methods for few-shot natural language understanding
Yanan Zheng, Jing Zhou, Yujie Qian, Ming Ding, Jian Li, Ruslan Salakhutdinov, Jie Tang, Sebastian Ruder, and Zhilin Yang. 2021 · 2021
Closest in time.
Adapting language models for zero-shot learning by meta-tuning on dataset and prompt collections
Ruiqi Zhong, Kristy Lee, Zheng Zhang, and Dan Klein. 2021 · 2021
Closest in time.