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Large language models have shown promising results in zero-shot settings (Brown et al.,2020; Radford et al., 2019).
Exploiting cloze questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2020a · 2001
Earlier work this paper cites.
Learning question classifiers
Xin Li and Dan Roth. 2002 · 2002
Earlier work this paper cites.
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
Earlier work this paper cites.
The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
Earlier work this paper cites.
Unifiedqa: Crossing format boundaries with a single qa system
Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi. 2020 · 2005
Earlier work this paper cites.
Redundancy and syntactic reduction in spontaneous speech
Tim Florian Jaeger. 2006 · 2006
Earlier work this paper cites.
Towards a clarification of probability, possibility and plausibility: how semantics could help futures practice to improve
Ruud Van der Helm. 2006 · 2006
Earlier work this paper cites.
Speakers optimize information density through syntactic reduction
Roger Levy and T Florian Jaeger. 2007 · 2007
Earlier work this paper cites.
ISP: Learning inferential selectional preferences
Patrick Pantel, Rahul Bhagat, Bonaventura Coppola, Timothy Chklovski, and Eduard Hovy. 2007 · 2007
Earlier work this paper cites.
It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2020c · 2009
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A flexible, corpus-driven model of regular and inverse selectional preferences
Katrin Erk, Sebastian Padó, and Ulrike Padó. 2010 · 2010
Earlier work this paper cites.
Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S Gordon. 2011 · 2011
Earlier work this paper cites.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2020 · 2012
Earlier work this paper cites.
How can we know when language models know?
Zhengbao Jiang, Jun Araki, Haibo Ding, and Graham Neubig. 2020a · 2012
Earlier work this paper cites.
Few-shot text generation with pattern-exploiting training
Timo Schick and Hinrich Schütze. 2020b · 2012
Earlier work this paper cites.
Reporting bias and knowledge acquisition
Jonathan Gordon and Benjamin Van Durme. 2013 · 2013
Earlier work this paper cites.
Youtube2text: Recognizing and describing arbitrary activities using semantic hierarchies and zero-shot recognition
Sergio Guadarrama, Niveda Krishnamoorthy, Girish Malkarnenkar, Subhashini Venugopalan, Raymond Mooney, Trevor Darrell, and Kate Saenko. 2013 · 2013
Earlier work this paper cites.
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
Earlier work this paper cites.
An embarrassingly simple approach to zero-shot learning
Bernardino Romera-Paredes and Philip Torr. 2015 · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
Cited alongside, same era.
A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and William B Dolan. 2016 · 2016
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Sequence to backward and forward sequences: A content-introducing approach to generative short-text conversation
Lili Mou, Yiping Song, Rui Yan, Ge Li, Lu Zhang, and Zhi Jin. 2016 · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Cited alongside, same era.
Race: Large-scale reading comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017
Cited alongside, same era.
Lsdsem 2017 shared task: The story cloze test
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
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Target-guided open-domain conversation
Jianheng Tang, Tiancheng Zhao, Chenyan Xiong, Xiaodan Liang, Eric Xing, and Zhiting Hu. 2019 · 2019
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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
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Unsupervised context rewriting for open domain conversation
Kun Zhou, Kai Zhang, Yu Wu, Shujie Liu, and Jingsong Yu. 2019 · 2019
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Maxwell Forbes, and Yejin Choi. 2020 · 2020
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Nasrin Mostafazadeh, Michael Roth, Annie Louis, Nathanael Chambers, and James Allen. 2017 · 2017
Cited alongside, same era.
Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
Cited alongside, same era.
Towards implicit content-introducing for generative short-text conversation systems
Lili Yao, Yaoyuan Zhang, Yansong Feng, Dongyan Zhao, and Rui Yan. 2017 · 2017
Cited alongside, same era.
Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
Cited alongside, same era.
Communicative efficiency, uniform information density, and the rational speech act theory
Roger Levy. 2018 · 2018
Cited alongside, same era.
Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018 · 2018
Cited alongside, same era.
Modeling semantic plausibility by injecting world knowledge
Su Wang, Greg Durrett, and Katrin Erk. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
DLGNet: A transformer-based model for dialogue response generation
Olabiyi Oluwatobi and Erik Mueller. 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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Neural machine translation with byte-level subwords
Changhan Wang, Kyunghyun Cho, and Jiatao Gu. 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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Warp: Word-level adversarial reprogramming
Karen Hambardzumyan, Hrant Khachatrian, and Jonathan May. 2021 · 2021
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2021 · 2021
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True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021 · 2021
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Prompt programming for large language models: Beyond the few-shot paradigm
Laria Reynolds and Kyle McDonell. 2021 · 2021
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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. 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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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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Adapting language models for zero-shot learning by meta-tuning on dataset and prompt collections
Ruiqi Zhong, Kristy Lee, Zheng Zhang, and Dan Klein. 2021 · 2021
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