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Large language models (LLMs) with in-context learning have demonstrated remarkable capability in the text-to-SQL task.
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.
Representing schema structure with graph neural networks for text-to-sql parsing
Ben Bogin, Matt Gardner, and Jonathan Berant. 2019 · 1905
Earlier work this paper cites.
Towards complex text-to-sql in cross-domain database with intermediate representation
Jiaqi Guo, Zecheng Zhan, Yan Gao, Yan Xiao, Jian-Guang Lou, Ting Liu, and Dongmei Zhang. 2019 · 1905
Earlier work this paper cites.
Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers
Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, and Matthew Richardson. 2019 · 1911
Earlier work this paper cites.
Evaluation of spoken language systems: The atis domain
Patti Price. 1990 · 1990
Earlier work this paper cites.
Expanding the scope of the atis task: The atis-3 corpus
Deborah A. Dahl, Madeleine Bates, Michael Brown, William Fisher, Kate Hunicke-Smith, David Pallett, Christine Pao, Alexander Rudnicky, and Elizabeth Shriberg. 1994 · 1994
Earlier work this paper cites.
Learning to parse database queries using inductive logic programming
John M. Zelle and Raymond J. Mooney. 1996 · 1996
Earlier work this paper cites.
Peter Shaw, Ming-Wei Chang, Panupong Pasupat, and Kristina Toutanova. 2020 · 2010
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Improving text-to-sql evaluation methodology
Catherine Finegan-Dollak, Jonathan K Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, and Dragomir Radev. 2018 · 2018
Earlier work this paper cites.
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, et al. 2018 · 2018
Cited alongside, same era.
Bridging textual and tabular data for cross-domain text-to-sql semantic parsing
Xi Victoria Lin, Richard Socher, and Caiming Xiong. 2020 · 2020
Cited alongside, same era.
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.
Lgesql: line graph enhanced text-to-sql model with mixed local and non-local relations
Ruisheng Cao, Lu Chen, Zhi Chen, Yanbin Zhao, Su Zhu, and Kai Yu. 2021 · 2021
Cited alongside, same era.
Natural sql: Making sql easier to infer from natural language specifications
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022 · 2022
Later among the works it cites.
Diverse demonstrations improve in-context compositional generalization
Itay Levy, Ben Bogin, and Jonathan Berant. 2022 · 2022
Later among the works it cites.
Synchromesh: Reliable code generation from pre-trained language models
Gabriel Poesia, Oleksandr Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani. 2022 · 2022
Later among the works it cites.
Evaluating the text-to-sql capabilities of large language models
Nitarshan Rajkumar, Raymond Li, and Dzmitry Bahdanau. 2022 · 2022
Later among the works it cites.
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Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021a
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Shadowgnn: Graph projection neural network for text-to-sql parser
Zhi Chen, Lu Chen, Yanbin Zhao, Ruisheng Cao, Zihan Xu, Su Zhu, and Kai Yu. 2021b
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Peng Shi, Rui Zhang, He Bai, and Jimmy Lin. 2022 · 2022
Later among the works it cites.
Dr. spider: A diagnostic evaluation benchmark towards text-to-sql robustness
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