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Recent foundational language models have shown state-of-the-art performance in many NLP tasks in zero- and few-shot settings.
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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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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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019 · 1910
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Semeval-2016 task 4: Sentiment analysis in twitter
Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, and Veselin Stoyanov. 2019 · 1912
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Newsweeder: Learning to filter netnews
Ken Lang. 1995 · 1995
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Text categorization with support vector machines: Learning with many relevant features
Thorsten Joachims. 1998 · 1998
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An evaluation of statistical approaches to text categorization
Yiming Yang. 1999 · 1999
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Rcv1: A new benchmark collection for text categorization research
David D Lewis, Yiming Yang, Tony Russell-Rose, and Fan Li. 2004 · 2004
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The turking test: Can language models understand instructions?
Avia Efrat and Omer Levy. 2020 · 2010
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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2010
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Learning word vectors for sentiment analysis
Andrew L Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts. 2011 · 2011
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Automatic semantic classification of scientific literature according to the hallmarks of cancer
Simon Baker, Ilona Silins, Yufan Guo, Imran Ali, Johan Högberg, Ulla Stenius, and Anna Korhonen. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Short text classification based on feature extension using the n-gram model
Xinwei Zhang and Bin Wu. 2015 · 2015
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Deceiving google’s perspective api built for detecting toxic comments. arxiv 2017
Hossein Hosseini, Sreeram Kannan, Baosen Zhang, and Radha Poovendran. 2017 · 2017
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Bag of tricks for efficient text classification
Armand Joulin, Édouard Grave, Piotr Bojanowski, and Tomáš Mikolov. 2017 · 2017
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Towards a seamless integration of word senses into downstream nlp applications
Mohammad Taher Pilehvar, Jose Camacho-Collados, Roberto Navigli, and Nigel Collier. 2017 · 2017
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Semeval 2018 task 2: Multilingual emoji prediction
Francesco Barbieri, Jose Camacho-Collados, Francesco Ronzano, Luis Espinosa Anke, Miguel Ballesteros, Valerio Basile, Viviana Patti, and Horacio Saggion. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. 2018 · 2018
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Semeval-2018 task 3: Irony detection in english tweets
Cynthia Van Hee, Els Lefever, and Véronique Hoste. 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 R Bowman. 2018 · 2018
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Semeval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter
Valerio Basile, Cristina Bosco, Elisabetta Fersini, Debora Nozza, Viviana Patti, Francisco Manuel Rangel Pardo, Paolo Rosso, and Manuela Sanguinetti. 2019 · 2019
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Large-scale multi-label text classification on eu legislation
Ilias Chalkidis, Emmanouil Fergadiotis, Prodromos Malakasiotis, and Ion Androutsopoulos. 2019 · 2019
Cited alongside, same era.
Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
Cited alongside, same era.
A study of various text augmentation techniques for relation classification in free text
Praveen Kumar Badimala Giridhara, Chinmaya Mishra, Reddy Kumar Modam Venkataramana, Syed Saqib Bukhari, and Andreas Dengel. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Cited alongside, same era.
Generalized zero-and few-shot learning via aligned variational autoencoders
Edgar Schönfeld, Sayna Ebrahimi, Samarth Sinha, Trevor Darrell, and Zeynep Akata. 2019 · 2019
Cited alongside, same era.
Few-shot and zero-shot approaches to legal text classification: A case study in the financial sector
Rajdeep Sarkar, Atul Kr. Ojha, Jay Megaro, John Mariano, Vall Herard, and John P. McCrae. 2021 · 2021
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2021b · 2021
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Generalized zero-shot text classification for icd coding
Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, and Eric Xing. 2021 · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021
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Gpt-neox-20b: An open-source autoregressive language model
Sidney Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, et al. 2022 · 2022
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Energy and policy considerations for deep learning in nlp
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 2019
Cited alongside, same era.
Knowledge-based short text categorization using entity and category embedding
Rima Türker, Lei Zhang, Maria Koutraki, and Harald Sack. 2019 · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach
Wenpeng Yin, Jamaal Hay, and Dan Roth. 2019 · 2019
Cited alongside, same era.
Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval)
Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar. 2019 · 2019
Cited alongside, same era.
Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019 · 2019
Cited alongside, same era.
An empirical study on large-scale multi-label text classification including few and zero-shot labels
Ilias Chalkidis, Manos Fergadiotis, Sotiris Kotitsas, Prodromos Malakasiotis, Nikolaos Aletras, and Ion Androutsopoulos. 2020 · 2020
Cited alongside, same era.
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
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Rlprompt: Optimizing discrete text prompts with reinforcement learning
Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric Xing, and Zhiting Hu. 2022 · 2022
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Guiding generative language models for data augmentation in few-shot text classification
Aleksandra Edwards, Asahi Ushio, Jose Camacho-Collados, Helene Ribaupierre, and Alun Preece. 2022 · 2022
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Few-shot learning for medical text: A systematic review
Yao Ge, Yuting Guo, Yuan-Chi Yang, Mohammed Ali Al-Garadi, and Abeed Sarker. 2022 · 2022
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Zero-shot text classification with self-training
Ariel Gera, Alon Halfon, Eyal Shnarch, Yotam Perlitz, Liat Ein-Dor, and Noam Slonim. 2022 · 2022
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Cross-task generalization via natural language crowdsourcing instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi. 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 Ray, et al. 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, Teven Le Scao, Arun Raja, et al. 2022 · 2022
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Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, et al. 2022b · 2022
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Learn to adapt for generalized zero-shot text classification
Yiwen Zhang, Caixia Yuan, Xiaojie Wang, Ziwei Bai, and Yongbin Liu. 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, et al. 2023 · 2023
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Yanis Labrak, Mickael Rouvier, and Richard Dufour. 2023 · 2023
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Gpteval: Nlg evaluation using gpt-4 with better human alignment
Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023 · 2023
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Few-shot fine-tuning vs. in-context learning: A fair comparison and evaluation
Marius Mosbach, Tiago Pimentel, Shauli Ravfogel, Dietrich Klakow, and Yanai Elazar. 2023 · 2023
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Large language models vote: Prompting for rare disease identification
David Oniani, Jordan Hilsman, Hang Dong, Fengyi Gao, Shiven Verma, and Yanshan Wang. 2023 · 2023
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Leveraging label variation in large language models for zero-shot text classification
Flor Miriam Plaza-del Arco, Debora Nozza, and Dirk Hovy. 2023 · 2023
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Evaluating the zero-shot robustness of instruction-tuned language models
Jiuding Sun, Chantal Shaib, and Byron C Wallace. 2023 · 2023
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Prompt2model: Generating deployable models from natural language instructions
Vijay Viswanathan, Chenyang Zhao, Amanda Bertsch, Tongshuang Wu, and Graham Neubig. 2023 · 2023
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