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Recent advances in zero-shot and few-shot learning have shown promise for a scope of research and practical purposes.
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
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On the Specification of Term Values in Automatic Indexing
Gerard Salton and C. S. Yang. 1973 · 1973
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Maximum likelihood estimation of observer error-rates using the em algorithm
Alexander Philip Dawid and Allan M Skene. 1979 · 1979
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Types of ethical theory
James Martineau. 2006 · 2006
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011 · 2011
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How to Grow a Mind: Statistics, Structure, and Abstraction
Joshua B Tenenbaum, Charles Kemp, Thomas L Griffiths, and Noah D Goodman. 2011 · 2011
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The winograd schema challenge
Hector Levesque, Ernest Davis, and Leora Morgenstern. 2012 · 2012
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WebVectors: A Toolkit for Building Web Interfaces for Vector Semantic Models , pages 155–161. Springer International Publishing, Cham
Andrey Kutuzov and Elizaveta Kuzmenko. 2017 · 2017
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To the methodology of corpus construction for machine learning:«taiga» syntax tree corpus and parser
Tatiana Shavrina and Olga Shapovalova. 2017 · 2017
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HotFlip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2018 · 2018
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WorldTree: A corpus of explanation graphs for elementary science questions supporting multi-hop inference
Peter Jansen, Elizabeth Wainwright, Steven Marmorstein, and Clayton Morrison. 2018 · 2018
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Deep text classification can be fooled
Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, and Wenchang Shi. 2018 · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
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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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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2019 · 2019
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EDA: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou. 2019 · 2019
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BERTScore: Evaluating Text Generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019 · 2019
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Self-supervised meta-learning for few-shot natural language classification tasks
Trapit Bansal, Rishikesh Jha, Tsendsuren Munkhdalai, and Andrew McCallum. 2020b · 2020
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The Costs of Connection: How Data Are Colonizing Human Life and Appropriating It for Capitalism
Nick Couldry and Ulises A Mejias. 2020 · 2020
Cited alongside, same era.
SberQuAD – russian reading comprehension dataset: Description and analysis
Pavel Efimov, Andrey Chertok, Leonid Boytsov, and Pavel Braslavski. 2020 · 2020
Cited alongside, same era.
Read and reason with MuSeRC and RuCoS: Datasets for machine reading comprehension for Russian
Alena Fenogenova, Vladislav Mikhailov, and Denis Shevelev. 2020 · 2020
Cited alongside, same era.
DaNetQA: a yes/no Question Answering Dataset for the Russian Language
Taisia Glushkova, Alexey Machnev, Alena Fenogenova, Tatiana Shavrina, Ekaterina Artemova, and Dmitry I Ignatov. 2020 · 2020
Cited alongside, same era.
Is BERT Really Robust? A Strong Baseline For Natural Language Attack On Text Classification And Entailment
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2020 · 2020
Cited alongside, same era.
Few-Shot Learning Evaluation in Natural Language Understanding
Subhabrata Mukherjee, Xiaodong Liu, Guoqing Zheng, Saghar Hosseini, Hao Cheng, Greg Yang, Christopher Meek, Ahmed Hassan Awadallah, and Jianfeng Gao. 2021 · 2021
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Crowdspeech and vox diy: Benchmark dataset for crowdsourced audio transcription
Nikita Pavlichenko, Ivan Stelmakh, and Dmitry Ustalov. 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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AI and the Everything in the Whole Wide World Benchmark
Inioluwa Deborah Raji, Emily Denton, Emily M Bender, Alex Hanna, and Amandalynne Paullada. 2021 · 2021
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Rubq 2.0: an innovated russian question answering dataset
Ivan Rybin, Vladislav Korablinov, Pavel Efimov, and Pavel Braslavski. 2021 · 2021
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It’s not just size that matters: Small language models are also few-shot learners
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RussianSuperGLUE: A Russian language understanding evaluation benchmark
Tatiana Shavrina, Alena Fenogenova, Emelyanov Anton, Denis Shevelev, Ekaterina Artemova, Valentin Malykh, Vladislav Mikhailov, Maria Tikhonova, Andrey Chertok, and Andrey Evlampiev. 2020 · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
Cited alongside, same era.
Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey
Wei Emma Zhang, Quan Z Sheng, Ahoud Alhazmi, and Chenliang Li. 2020 · 2020
Cited alongside, same era.
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare. 2021 · 2021
Cited alongside, same era.
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, et al. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
What will it take to fix benchmarking in natural language understanding?
Samuel R. Bowman and George Dahl. 2021 · 2021
Cited alongside, same era.
Timo Schick and Hinrich Schütze. 2021 · 2021
Later among the works it cites.
Improving and simplifying pattern exploiting training
Derek Tam, Rakesh R. Menon, Mohit Bansal, Shashank Srivastava, and Colin Raffel. 2021 · 2021
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Disembodied Machine Learning: On the Illusion of Objectivity in NLP
Zeerak Waseem, Smarika Lulz, Joachim Bingel, and Isabelle Augenstein. 2021 · 2021
Later among the works it cites.
Language models are few-shot multilingual learners
Genta Indra Winata, Andrea Madotto, Zhaojiang Lin, Rosanne Liu, Jason Yosinski, and Pascale Fung. 2021 · 2021
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FewCLUE: A chinese few-shot learning evaluation benchmark
Liang Xu, Xiaojing Lu, Chenyang Yuan, Xuanwei Zhang, Huilin Xu, Hu Yuan, Guoao Wei, Xiang Pan, Xin Tian, Libo Qin, et al. 2021 · 2021
Later among the works it cites.
Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation
Usama Yaseen and Stefan Langer. 2021 · 2021
Later among the works it cites.
CrossFit: A few-shot learning challenge for cross-task generalization in NLP
Qinyuan Ye, Bill Yuchen Lin, and Xiang Ren. 2021 · 2021
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Mapping global dynamics of benchmark creation and saturation in artificial intelligence
Adriano Barbosa-Silva, Simon Ott, Kathrin Blagec, Jan Brauner, and Matthias Samwald. 2022 · 2022
Closest in time.
Zero- and few-shot NLP with pretrained language models
Iz Beltagy, Arman Cohan, Robert Logan IV, Sewon Min, and Sameer Singh. 2022 · 2022
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Russia and the diffusion of political norms: the perfect rival?
Tom Casier. 2022 · 2022
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Emerging Trends: SOTA-Chasing
Kenneth Ward Church and Valia Kordoni. 2022 · 2022
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On the machine learning of ethical judgments from natural language
Zeerak Talat, Hagen Blix, Josef Valvoda, Maya Indira Ganesh, Ryan Cotterell, and Adina Williams. 2022 · 2022
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Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, et al. 2022 · 2022
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FewNLU: Benchmarking state-of-the-art methods for few-shot natural language understanding
Yanan Zheng, Jing Zhou, Yujie Qian, Ming Ding, Chonghua Liao, Li Jian, Ruslan Salakhutdinov, Jie Tang, Sebastian Ruder, and Zhilin Yang. 2022 · 2022
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
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