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A major challenge in Natural Language Processing is obtaining annotated data for supervised learning.
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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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Data quality from crowdsourcing: A study of annotation selection criteria
Pei-Yun Hsueh, Prem Melville, and Vikas Sindhwani. 2009 · 2009
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How reliable are annotations via crowdsourcing: a study about inter-annotator agreement for multi-label image annotation
Stefanie Nowak and Stefan Rüger. 2010 · 2010
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Active learning with sampling by uncertainty and density for data annotations
Jingbo Zhu, Huizhen Wang, Benjamin K. Tsou, and Matthew Ma. 2010 · 2010
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Personalizing automated image annotation using cross-entropy
X. Li, E. Gavves, C. G. M. Snoek, M. Worring, and A. W. M. Smeulders. 2011 · 2011
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Density-based clustering based on hierarchical density estimates
Ricardo J. G. B. Campello, Davoud Moulavi, and Joerg Sander. 2013 · 2013
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Learning whom to trust with MACE
Dirk Hovy, Taylor Berg-Kirkpatrick, Ashish Vaswani, and Eduard Hovy. 2013 · 2013
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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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Oscar Beijbom. 2014 · 2014
Cited alongside, same era.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Adversarial Active Learning for Deep Networks: a Margin Based Approach
Melanie Ducoffe and Frederic Precioso. 2018 · 2018
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Query-by-committee improvement with diversity and density in batch active learning
Seho Kee, Enrique del Castillo, and George Runger. 2018 · 2018
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CARER: Contextualized affect representations for emotion recognition
Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, Junlin Wu, and Yi-Shin Chen. 2018 · 2018
Cited alongside, same era.
DEBACER: a method for slicing moderated debates
Thomas Palmeira Ferraz, Alexandre Alcoforado, Enzo Bustos, André Seidel Oliveira, Rodrigo Gerber, Naíde Müller, André Corrêa d’Almeida, Bruno Miguel Veloso, and Anna Helena Reali Costa. 2021 · 2021
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The perils of using Mechanical Turk to evaluate open-ended text generation
Marzena Karpinska, Nader Akoury, and Mohit Iyyer. 2021 · 2021
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A Survey of Deep Active Learning
Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B. Gupta, Xiaojiang Chen, and Xin Wang. 2021 · 2021
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A stochastic process discretization method combing active learning kriging model for efficient time-variant reliability analysis
Dequan Zhang, Pengfei Zhou, Chen Jiang, Meide Yang, Xu Han, and Qing Li. 2021 · 2021
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ZeroBERTo: Leveraging Zero-Shot Text Classification by Topic Modeling
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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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Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 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.
Probabilistic Ensembles of Zero- and Few-Shot Learning Models for Emotion Classification
Angelo Basile, Guillermo Pérez-Torró, and Marc Franco-Salvador. 2021 · 2021
Cited alongside, same era.
A needle in a haystack: An analysis of high-agreement workers on MTurk for summarization
Lining Zhang, Simon Mille, Yufang Hou, Daniel Deutsch, Elizabeth Clark, Yixin Liu, Saad Mahamood, Sebastian Gehrmann, Miruna Clinciu, Khyathi Raghavi Chandu, and João Sedoc. 2023a
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Reformulating domain adaptation of large language models as adapt-retrieve-revise
Yating Zhang, Yexiang Wang, Fei Cheng, Sadao Kurohashi, et al. 2023b
Cited in the paper.
Alexandre Alcoforado, Thomas Palmeira Ferraz, Rodrigo Gerber, Enzo Bustos, André Seidel Oliveira, Bruno Miguel Veloso, Fabio Levy Siqueira, and Anna Helena Reali Costa. 2022 · 2022
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Efficient few-shot learning without prompts
Lewis Tunstall, Nils Reimers, Unso Eun Seo Jo, Luke Bates, Daniel Korat, Moshe Wasserblat, and Oren Pereg. 2022 · 2022
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Thomas Palmeira Ferraz, Marcely Zanon Boito, Caroline Brun, and Vassilina Nikoulina. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
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Harnessing the power of llms in practice: A survey on chatgpt and beyond
Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, and Xia Hu. 2023 · 2023
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