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We investigate the dynamics of increasing the number of model parameters versus the number of labeled examples across a wide variety of tasks.
UNIFIEDQA: Crossing format boundaries with a single QA system
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi. 2020 · 1907
Earlier work this paper cites.
Learning to Learn: Introduction and Overview , page 3–17. Kluwer Academic Publishers, USA
Sebastian Thrun and Lorien Pratt. 1998 · 1998
Earlier work this paper cites.
Object classification from a single example utilizing class relevance metrics
Michael Fink. 2005 · 2005
Earlier work this paper cites.
The second pascal recognising textual entailment challenge
Roy Bar-Haim, Ido Dagan, Bill Dolan, L. Ferro, Danilo Giampiccolo, and B. Magnini. 2006 · 2006
Earlier work this paper cites.
The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006 · 2006
Earlier work this paper cites.
Ontonotes: The 90
Eduard Hovy, Mitchell Marcus, Martha Palmer, Lance Ramshaw, and Ralph Weischedel. 2006 · 2006
Earlier work this paper cites.
One-shot learning of object categories
Fei-Fei Li, R. Fergus, and P. Perona. 2006 · 2006
Earlier work this paper cites.
The third pascal recognizing textual entailment challenge
Danilo Giampiccolo, B. Magnini, Ido Dagan, and W. Dolan. 2007 · 2007
Earlier work this paper cites.
Meta-learning for few-shot natural language processing: A survey
Wenpeng Yin. 2020 · 2007
Earlier work this paper cites.
The sixth pascal recognizing textual entailment challenge
L. Bentivogli, Peter Clark, Ido Dagan, and Danilo Giampiccolo. 2009 · 2009
Earlier work this paper cites.
It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and H. Schutze. 2021 · 2009
Earlier work this paper cites.
Few-shot text generation with pattern-exploiting training
Timo Schick and H. Schutze. 2020 · 2012
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 Ng, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
Ask me anything: Dynamic memory networks for natural language processing
Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, and Richard Socher. 2016 · 2016
Earlier work this paper cites.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, koray kavukcuoglu, and Daan Wierstra. 2016 · 2016
Earlier work this paper cites.
Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
Earlier work this paper cites.
TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
Earlier work this paper cites.
Zero-shot relation extraction via reading comprehension
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017 · 2017
Earlier work this paper cites.
Inference is everything: Recasting semantic resources into a unified evaluation framework
Aaron Steven White, Pushpendre Rastogi, Kevin Duh, and Benjamin Van Durme. 2017 · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018
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On the importance of attention in meta-learning for few-shot text classification
Xiang Jiang, Mohammad Havaei, Gabriel Chartrand, Hassan Chouaib, Thomas Vincent, Andrew Jesson, Nicolas Chapados, and Stan Matwin. 2018 · 2018
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How much reading does reading comprehension require? a critical investigation of popular benchmarks
Divyansh Kaushik and Zachary C. Lipton. 2018 · 2018
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The natural language decathlon: Multitask learning as question answering
Bryan McCann, N. Keskar, Caiming Xiong, and R. Socher. 2018 · 2018
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Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018 · 2018
How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 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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How much knowledge can you pack into the parameters of a language model?
Adam Roberts, Colin Raffel, and Noam Shazeer. 2020 · 2020
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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 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 Bowman. 2018 · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
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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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DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
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MRQA 2019 shared task: Evaluating generalization in reading comprehension
Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, and Danqi Chen. 2019 · 2019
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Parameter-efficient transfer learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
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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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Assessing the benchmarking capacity of machine reading comprehension datasets
Saku Sugawara, Pontus Stenetorp, Kentaro Inui, and Akiko Aizawa. 2020 · 2020
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CorefQA: Coreference resolution as query-based span prediction
Wei Wu, Fei Wang, Arianna Yuan, Fei Wu, and Jiwei Li. 2020 · 2020
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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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Question and answer test-train overlap in open-domain question answering datasets
Patrick Lewis, Pontus Stenetorp, and Sebastian Riedel. 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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Cutting down on prompts and parameters: Simple few-shot learning with language models
IV Robert L. Logan, Ivana Balavzevi’c, Eric Wallace, Fabio Petroni, Sameer Singh, and Sebastian Riedel. 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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Learning how to ask: Querying lms with mixtures of soft prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
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Few-shot question answering by pretraining span selection
Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, and Omer Levy. 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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Improving and simplifying pattern exploiting training
Derek Tam, Rakesh R Menon, Mohit Bansal, Shashank Srivastava, and Colin Raffel. 2021 · 2021
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Factual probing is [MASK]: learning vs. learning to recall
Zexuan Zhong, Dan Friedman, and Danqi Chen. 2021 · 2021
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