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Prompting large language models (LLMs) for data augmentation has recently become a common practice in few-shot NLP tasks.
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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Liii. on lines and planes of closest fit to systems of points in space
Karl Pearson F.R.S. 1901 · 1901
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Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
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Introduction to Information Retrieval
Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 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 Y. Ng, and Christopher Potts. 2013 · 2013
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Semeval-2014 task 4: Aspect based sentiment analysis
Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. 2014 · 2014
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015 · 2015
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Generating sentences from a continuous space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, and Samy Bengio. 2016 · 2016
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Invertible conditional gans for image editing
Guim Perarnau, Joost van de Weijer, Bogdan Raducanu, and José M. Álvarez. 2016 · 2016
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Data augmentation for low-resource neural machine translation
Marzieh Fadaee, Arianna Bisazza, and Christof Monz. 2017 · 2017
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Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P. Xing. 2017 · 2017
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2018 · 2018
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ELEGANT: exchanging latent encodings with GAN for transferring multiple face attributes
Taihong Xiao, Jiapeng Hong, and Jinwen Ma. 2018 · 2018
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Learning to flip the sentiment of reviews from non-parallel corpora
Canasai Kruengkrai. 2019a · 2019
Cited alongside, same era.
Learning to flip the sentiment of reviews from non-parallel corpora
Canasai Kruengkrai. 2019b · 2019
Cited alongside, same era.
Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 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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Tweeteval: Unified benchmark and comparative evaluation for tweet classification
Francesco Barbieri, José Camacho-Collados, Luis Espinosa Anke, and Leonardo Neves. 2020 · 2020
Cited alongside, same era.
Commongen: A constrained text generation challenge for generative commonsense reasoning
A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui. 2022 · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and L. Sifre. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Gray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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Flipda: Effective and robust data augmentation for few-shot learning
Jing Zhou, Yanan Zheng, Jie Tang, Li Jian, and Zhilin Yang. 2022 · 2022
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Bill Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, and Xiang Ren. 2020 · 2020
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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, Wei Li, and Peter J. Liu. 2020 · 2020
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Interpreting the latent space of gans for semantic face editing
Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou. 2020 · 2020
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Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
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Closed-form factorization of latent semantics in gans
Yujun Shen and Bolei Zhou. 2021 · 2021
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FUDGE: controlled text generation with future discriminators
Kevin Yang and Dan Klein. 2021 · 2021
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Gpt3mix: Leveraging large-scale language models for text augmentation
Kang Min Yoo, Dongju Park, Jaewook Kang, Sang-Woo Lee, and Woomyeong Park. 2021 · 2021
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Improving contrastive learning of sentence embeddings from ai feedback
Qinyuan Cheng, Xiaogui Yang, Tianxiang Sun, Linyang Li, and Xipeng Qiu. 2023 · 2023
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Chataug: Leveraging chatgpt for text data augmentation
Haixing Dai, Zhengliang Liu, Wenxiong Liao, Xiaoke Huang, Zihao Wu, Lin Zhao, Wei Liu, Ninghao Liu, Sheng Li, Dajiang Zhu, Hongmin Cai, Quanzheng Li, Tianming Liu, and Xiang Li. 2023 · 2023
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A survey on in-context learning
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Dail: Data augmentation for in-context learning via self-paraphrase
Dawei Li, Yaxuan Li, Dheeraj Mekala, Shuyao Li, Yulin wang, Xueqi Wang, William Hogan, and Jingbo Shang. 2023 · 2023
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Selective in-context data augmentation for intent detection using pointwise V-information
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MTEB: massive text embedding benchmark
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