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Recently Large Language Models (LLMs) have been proven to have strong abilities in various domains and tasks.
CoSQL: A conversational text-to-SQL challenge towards cross-domain natural language interfaces to databases
Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, and Dragomir Radev. 2019a · 1979
Earlier work this paper cites.
Sqlnet: Generating structured queries from natural language without reinforcement learning
Xiaojun Xu, Chang Liu, and Dawn Song. 2017 · 2017
Earlier work this paper cites.
Seq2sql: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher. 2017 · 2017
Earlier work this paper cites.
Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir Radev. 2018 · 2018
Earlier work this paper cites.
Representing schema structure with graph neural networks for text-to-SQL parsing
Ben Bogin, Jonathan Berant, and Matt Gardner. 2019 · 2019
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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 · 2020
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. 2020 · 2020
Earlier work this paper cites.
Grounded adaptation for zero-shot executable semantic parsing
Victor Zhong, Mike Lewis, Sida I. Wang, and Luke Zettlemoyer. 2020 · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
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 · 2021
Cited alongside, same era.
Structure-grounded pretraining for text-to-SQL
Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek, Oleksandr Polozov, Huan Sun, and Matthew Richardson. 2021 · 2021
Cited alongside, same era.
Exploring underexplored limitations of cross-domain text-to-SQL generalization
Yujian Gan, Xinyun Chen, and Matthew Purver. 2021b · 2021
Cited alongside, same era.
PICARD: Parsing incrementally for constrained auto-regressive decoding from language models
Torsten Scholak, Nathan Schucher, and Dzmitry Bahdanau. 2021 · 2021
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
N-best hypotheses reranking for text-to-sql systems
Lu Zeng, Sree Hari Krishnan Parthasarathi, and Dilek Hakkani-Tur. 2022 · 2022
Later among the works it cites.
A heterogeneous graph to abstract syntax tree framework for text-to-sql
Ruisheng Cao, Lu Chen, Jieyu Li, Hanchong Zhang, Hongshen Xu, Wangyou Zhang, and Kai Yu. 2023 · 2023
Closest in time.
How to prompt llms for text-to-sql: A study in zero-shot, single-domain, and cross-domain settings
Shuaichen Chang and Eric Fosler-Lussier. 2023 · 2023
Closest in time.
C3: Zero-shot text-to-sql with chatgpt
Xuemei Dong, Chao Zhang, Yuhang Ge, Yuren Mao, Yunjun Gao, Lu Chen, Jinshu Lin, and Dongfang Lou. 2023 · 2023
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Cited alongside, same era.
RASAT: Integrating relational structures into pretrained Seq2Seq model for text-to-SQL
Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, and Zhouhan Lin. 2022 · 2022
Cited alongside, same era.
Evaluating the text-to-sql capabilities of large language models
Nitarshan Rajkumar, Raymond Li, and Dzmitry Bahdanau. 2022 · 2022
Cited alongside, same era.
Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022 · 2022
Cited alongside, same era.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021a
Cited in the paper.
Towards robustness of text-to-SQL models against synonym substitution
Yujian Gan, Xinyun Chen, Qiuping Huang, Matthew Purver, John R. Woodward, Jinxia Xie, and Pengsheng Huang. 2021a
Cited in the paper.
Resdsql: Decoupling schema linking and skeleton parsing for text-to-sql
Haoyang Li, Jing Zhang, Cuiping Li, and Hong Chen. 2023a
Cited in the paper.
Dir: A large-scale dialogue rewrite dataset for cross-domain conversational text-to-sql
Jieyu Li, Zhi Chen, Lu Chen, Zichen Zhu, Hanqi Li, Ruisheng Cao, and Kai Yu. 2023b
Cited in the paper.
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2023 · 2023
Closest in time.
Linyong Nan, Yilun Zhao, Weijin Zou, Narutatsu Ri, Jaesung Tae, Ellen Zhang, Arman Cohan, and Dragomir Radev. 2023 · 2023
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Din-sql: Decomposed in-context learning of text-to-sql with self-correction
Mohammadreza Pourreza and Davood Rafiei. 2023 · 2023
Closest in time.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023 · 2023
Closest in time.