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Collecting high-quality labeled data for model training is notoriously time-consuming and labor-intensive for various NLP tasks.
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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A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
Bo Pang and Lillian Lee. 2004 · 2004
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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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Entropy-based active learning for object recognition
Alex Holub, Pietro Perona, and Michael C. Burl. 2008 · 2008
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An analysis of active learning strategies for sequence labeling tasks
Burr Settles and Mark Craven. 2008 · 2008
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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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Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean. 2015 · 2015
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Biocreative V CDR task corpus: a resource for chemical disease relation extraction
Jiao Li, Yueping Sun, Robin J. Johnson, Daniela Sciaky, Chih-Hsuan Wei, Robert Leaman, Allan Peter Davis, Carolyn J. Mattingly, Thomas C. Wiegers, and Zhiyong Lu. 2016 · 2016
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Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. 2017 · 2017
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor W. Tsang, and Masashi Sugiyama. 2018 · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese. 2018 · 2018
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert R. Sabuncu. 2018 · 2018
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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel. 2019 · 2019
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Sampling bias in deep active classification: An empirical study
Ameya Prabhu, Charles Dognin, and Maneesh Singh. 2019 · 2019
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Symmetric cross entropy for robust learning with noisy labels
Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey. 2019 · 2019
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Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. 2020 · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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 · 2020
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Active Learning for BERT: An Empirical Study
Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, and Noam Slonim. 2020 · 2020
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Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. 2020 · 2020
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Dividemix: Learning with noisy labels as semi-supervised learning
Junnan Li, Richard Socher, and Steven C. H. Hoi. 2020 · 2020
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Early-learning regularization prevents memorization of noisy labels
Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. 2020 · 2020
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Active sentence learning by adversarial uncertainty sampling in discrete space
Dongyu Ru, Jiangtao Feng, Lin Qiu, Hao Zhou, Mingxuan Wang, Weinan Zhang, Yong Yu, and Lei Li. 2020 · 2020
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Generating training data with language models: Towards zero-shot language understanding
Yu Meng, Jiaxin Huang, Yu Zhang, and Jiawei Han. 2022 · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M. Rush. 2022 · 2022
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. 2020 · 2020
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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
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Cold-start active learning through self-supervised language modeling
Michelle Yuan, Hsuan-Tien Lin, and Jordan Boyd-Graber. 2020 · 2020
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Active learning approaches to enhancing neural machine translation
Yuekai Zhao, Haoran Zhang, Shuchang Zhou, and Zhihua Zhang. 2020 · 2020
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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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Active learning by acquiring contrastive examples
Katerina Margatina, Giorgos Vernikos, Loïc Barrault, and Nikolaos Aletras. 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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Evaluating unsupervised text classification: Zero-shot and similarity-based approaches
Tim Schopf, Daniel Braun, and Florian Matthes. 2022 · 2022
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Language models in the loop: Incorporating prompting into weak supervision
Ryan Smith, Jason A. Fries, Braden Hancock, and Stephen H. Bach. 2022 · 2022
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Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Kathleen S. Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Agüera y Arcas, Claire Cui, Marian Croak, Ed H. Chi, and Quoc Le. 2022 · 2022
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Unsupervised selective labeling for more effective semi-supervised learning
Xudong Wang, Long Lian, and Stella X. Yu. 2022 · 2022
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2022 · 2022
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ZeroPrompt: Scaling prompt-based pretraining to 1,000 tasks improves zero-shot generalization
Hanwei Xu, Yujun Chen, Yulun Du, Nan Shao, Wang Yanggang, Haiyu Li, and Zhilin Yang. 2022 · 2022
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ZeroGen: Efficient zero-shot learning via dataset generation
Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, and Lingpeng Kong. 2022a · 2022
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ProGen: Progressive zero-shot dataset generation via in-context feedback
Jiacheng Ye, Jiahui Gao, Zhiyong Wu, Jiangtao Feng, Tao Yu, and Lingpeng Kong. 2022b · 2022
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Active learning through a covering lens
Ofer Yehuda, Avihu Dekel, Guy Hacohen, and Daphna Weinshall. 2022 · 2022
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AcTune: Uncertainty-based active self-training for active fine-tuning of pretrained language models
Yue Yu, Lingkai Kong, Jieyu Zhang, Rongzhi Zhang, and Chao Zhang. 2022 · 2022
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Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, Quyet V. Do, Yan Xu, and Pascale Fung. 2023 · 2023
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Self-guided noise-free data generation for efficient zero-shot learning
Jiahui Gao, Renjie Pi, Yong Lin, Hang Xu, Jiacheng Ye, Zhiyong Wu, Weizhong Zhang, Xiaodan Liang, Zhenguo Li, and Lingpeng Kong. 2023 · 2023
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OpenAI. 2023 · 2023
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Selective annotation makes language models better few-shot learners
Hongjin SU, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, and Tao Yu. 2023 · 2023
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