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Large pre-trained language models (LMs) such as GPT-3 have acquired a surprising ability to perform zero-shot learning.
UNIFIEDQA: Crossing format boundaries with a single QA system
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Well-read students learn better: On the importance of pre-training compact models
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Exploiting cloze questions for few-shot text classification and natural language inference
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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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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, et al. 2020 · 2005
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Meta-learning for few-shot natural language processing: A survey
Wenpeng Yin. 2020 · 2007
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Aligning AI With Shared Human Values
Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, and Jacob Steinhardt. 2020 · 2008
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2020b · 2009
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Customizing triggers with concealed data poisoning
Eric Wallace, Tony Z Zhao, Shi Feng, and Sameer Singh. 2020 · 2010
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Extracting training data from large language models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel. 2020 · 2012
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Towards sms spam filtering: Results under a new dataset
Tiago Almeida, José María Gómez Hidalgo, and Tiago Pasqualini Silva. 2013 · 2013
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016 · 2016
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SemEval-2016 task 6: Detecting stance in tweets
Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016 · 2016
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle. 2016 · 2016
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Train once, test anywhere: Zero-shot learning for text classification
Pushpankar Kumar Pushp and Muktabh Mayank Srivastava. 2017 · 2017
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SemEval-2017 task 4: Sentiment analysis in Twitter
Sara Rosenthal, Noura Farra, and Preslav Nakov. 2017 · 2017
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The natural language decathlon: Multitask learning as question answering
Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. 2018 · 2018
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Decomposing fit semantics for product size recommendation in metric spaces
Rishabh Misra, Mengting Wan, and Julian McAuley. 2018 · 2018
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SemEval-2018 task 1: Affect in tweets
Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, and Svetlana Kiritchenko. 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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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R Bowman. 2018 · 2018
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Ro{bert}a: 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. 2020 · 2020
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An investigation of why overparameterization exacerbates spurious correlations
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, and Percy Liang. 2020 · 2020
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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 · 2020
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SemEval-2020 task 6: Definition extraction from free text with the DEFT corpus
Sasha Spala, Nicholas Miller, Franck Dernoncourt, and Carl Dockhorn. 2020 · 2020
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Learning from task descriptions
Orion Weller, Nicholas Lourie, Matt Gardner, and Matthew Peters. 2020 · 2020
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel 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
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BoolQ: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 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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Designing and interpreting probes with control tasks
John Hewitt and Percy Liang. 2019 · 2019
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Robust neural machine translation with joint textual and phonetic embedding
Hairong Liu, Mingbo Ma, Liang Huang, Hao Xiong, and Zhongjun He. 2019 · 2019
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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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Meta-learning without memorization
Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, and Chelsea Finn. 2020 · 2020
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Muppet: Massive multi-task representations with pre-finetuning
Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, Xilun Chen, Luke Zettlemoyer, and Sonal Gupta. 2021 · 2021
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Flex: Unifying evaluation for few-shot nlp
Jonathan Bragg, Arman Cohan, Kyle Lo, and Iz Beltagy. 2021 · 2021
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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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Surface form competition: Why the highest probability answer isn’t always right
Ari Holtzman, Peter West, Vered Shwartz, Yejin Choi, and Luke Zettlemoyer. 2021 · 2021
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How many data points is a prompt worth?
Teven Le Scao and Alexander Rush. 2021 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Natural instructions: Benchmarking generalization to new tasks from natural language instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi. 2021 · 2021
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Dreca: A general task augmentation strategy for few-shot natural language inference
Shikhar Murty, Tatsunori B Hashimoto, and Christopher D Manning. 2021 · 2021
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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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Zero-shot learning by generating task-specific adapters
Qinyuan Ye and Xiang Ren. 2021 · 2021
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Calibrate before use: Improving few-shot performance of language models
Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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