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Recent literature has shown that large language models (LLMs) are generally excellent few-shot reasoners to solve text reasoning tasks.
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
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang. 2015 · 2015
Earlier work this paper cites.
Macro grammars and holistic triggering for efficient semantic parsing
Yuchen Zhang, Panupong Pasupat, and Percy Liang. 2017 · 2017
Earlier work this paper cites.
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Victor Zhong, Caiming Xiong, and Richard Socher. 2017 · 2017
Earlier work this paper cites.
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Earlier work this paper cites.
Memory augmented policy optimization for program synthesis and semantic parsing
Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc V Le, and Ni Lao. 2018 · 2018
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. 2019 · 2019
Earlier work this paper cites.
Learning semantic parsers from denotations with latent structured alignments and abstract programs
Bailin Wang, Ivan Titov, and Mirella Lapata. 2019 · 2019
Earlier work this paper cites.
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Wenhu Chen, Hanwen Zha, Zhiyu Chen, Wenhan Xiong, Hong Wang, and William Yang Wang. 2020c · 2020
Earlier work this paper cites.
Understanding tables with intermediate pre-training
Julian Eisenschlos, Syrine Krichene, and Thomas Mueller. 2020 · 2020
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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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, Peter J Liu, et al. 2020 · 2020
Cited alongside, same era.
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Alon Talmor, Ori Yoran, Amnon Catav, Dan Lahav, Yizhong Wang, Akari Asai, Gabriel Ilharco, Hannaneh Hajishirzi, and Jonathan Berant. 2020 · 2020
Cited alongside, same era.
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Xiaoyu Yang, Feng Nie, Yufei Feng, Quan Liu, Zhigang Chen, and Xiaodan Zhu. 2020 · 2020
Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Finqa: A dataset of numerical reasoning over financial data
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Cited alongside, same era.
Tapex: Table pre-training via learning a neural sql executor
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