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Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples.
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 R. Bowman · 1905
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Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach
Wenpeng Yin, Jamaal Hay, and Dan Roth · 1909
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 1910
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A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1997
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One-shot learning of object categories
Li Fei-Fei, R. Fergus, and P. Perona · 2006
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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 · 2007
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One shot learning of simple visual concepts
B. Lake, R. Salakhutdinov, Jason Gross, and J. Tenenbaum · 2011
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How to grow a mind: Statistics, structure, and abstraction
J. Tenenbaum, Charles Kemp, T. Griffiths, and Noah D. Goodman · 2011
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Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports
Harsha Gurulingappa, Abdul Mateen Rajput, Angus Roberts, Juliane Fluck, Martin Hofmann-Apitius, and Luca Toldo · 2012
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman · 2017
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OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification
Sowmya Vajjala and Ivana Lučić · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding, 2019
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Claudette: an automated detector of potentially unfair clauses in online terms of service
Marco Lippi, Przemysław Pałka, Giuseppe Contissa, Francesca Lagioia, Hans-Wolfgang Micklitz, Giovanni Sartor, and Paolo Torroni · 2019
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Automatically identifying complaints in social media
Daniel Preoţiuc-Pietro, Mihaela Gaman, and Nikolaos Aletras · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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TweetEval: Unified benchmark and comparative evaluation for tweet classification
Francesco Barbieri, Jose Camacho-Collados, Luis Espinosa Anke, and Leonardo Neves · 2020
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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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Ai ethics statements - analysis and lessons learnt from neurips broader impact statements
Carolyn Ashurst, Emmie Hine, Paul Sedille, and Alexis Carlier · 2021
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GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, March 2021
Sid Black, Gao Leo, Phil Wang, Connor Leahy, and Stella Biderman · 2021
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Flex: Unifying evaluation for few-shot nlp
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URL https://docs.google.com/spreadsheets/d/1CT3hCvbKxyJeS1FdrZtlK5MTuvWsfXR_
Dataset of affiliation types, 2021 · 2021
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Zero-shot learning in modern nlp
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Neurips affiliation locations, 2020
Sergey Ivanov · 2020
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What works to increase charitable donations? a meta-review with meta-meta-analysis, Feb 2020
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Tai safety bibliographic database, Dec 2020
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Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers, 2020
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“liar, liar pants on fire”: A new benchmark dataset for fake news detection
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