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Text-to-SQL benchmarks play a crucial role in evaluating the progress made in the field and the ranking of different models.
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.
The undecidable: Basic papers on undecidable propositions, unsolvable problems and computable functions
Martin Davis. 2004 · 2004
Earlier work this paper cites.
Grounded adaptation for zero-shot executable semantic parsing
Victor Zhong, Mike Lewis, Sida I Wang, and Luke Zettlemoyer. 2020b · 2009
Earlier work this paper cites.
Semantic evaluation for text-to-sql with distilled test suites
Ruiqi Zhong, Tao Yu, and Dan Klein. 2020a · 2010
Earlier work this paper cites.
Bridging textual and tabular data for cross-domain text-to-sql semantic parsing
Xi Victoria Lin, Richard Socher, and Caiming Xiong. 2020 · 2012
Earlier work this paper cites.
Seq2sql: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher. 2017 · 2017
Cited alongside, same era.
Re-examining the role of schema linking in text-to-sql
Wenqiang Lei, Weixin Wang, Zhixin Ma, Tian Gan, Wei Lu, Min-Yen Kan, and Tat-Seng Chua. 2020 · 2020
Cited alongside, same era.
Athena++ natural language querying for complex nested sql queries
Jaydeep Sen, Chuan Lei, Abdul Quamar, Fatma Özcan, Vasilis Efthymiou, Ayushi Dalmia, Greg Stager, Ashish Mittal, Diptikalyan Saha, and Karthik Sankaranarayanan. 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.
Picard: Parsing incrementally for constrained auto-regressive decoding from language models
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.
Active programming by example with a natural language prior
Ruiqi Zhong, Charlie Snell, Dan Klein, and Jason Eisner. 2022 · 2022
Later among the works it cites.
A comprehensive evaluation of chatgpt’s zero-shot text-to-sql capability
Aiwei Liu, Xuming Hu, Lijie Wen, and Philip S Yu. 2023 · 2023
Closest in time.
Din-sql: Decomposed in-context learning of text-to-sql with self-correction
Mohammadreza Pourreza and Davood Rafiei. 2023 · 2023
Closest in time.
Sql-palm: Improved large language modeladaptation for text-to-sql
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Torsten Scholak, Nathan Schucher, and Dzmitry Bahdanau. 2021 · 2021
Cited alongside, same era.
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.
Exploring underexplored limitations of cross-domain text-to-sql generalization
Yujian Gan, Xinyun Chen, and Matthew Purver. 2021b
Cited in the paper.
Graphix-t5: Mixing pre-trained transformers with graph-aware layers for text-to-sql parsing
Jinyang Li, Binyuan Hui, Reynold Cheng, Bowen Qin, Chenhao Ma, Nan Huo, Fei Huang, Wenyu Du, Luo Si, and Yongbin Li. 2023a
Cited in the paper.
Jinyang Li, Binyuan Hui, Ge Qu, Binhua Li, Jiaxi Yang, Bowen Li, Bailin Wang, Bowen Qin, Rongyu Cao, Ruiying Geng, et al. 2023b
Cited in the paper.
Syntaxsqlnet: Syntax tree networks for complex and cross-domaintext-to-sql task
Tao Yu, Michihiro Yasunaga, Kai Yang, Rui Zhang, Dongxu Wang, Zifan Li, and Dragomir Radev. 2018a
Cited in the paper.
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, et al. 2018b
Cited in the paper.
Ruoxi Sun, Sercan O Arik, Hootan Nakhost, Hanjun Dai, Rajarishi Sinha, Pengcheng Yin, and Tomas Pfister. 2023 · 2023
Closest in time.