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This paper proposes a ChatGPT-based zero-shot Text-to-SQL method, dubbed C3, which achieves 82.3\% in terms of execution accuracy on the holdout test set of Spider and becomes the state-of-the-art zero-shot Text-to-SQL method on the Spider Challenge.
Learning to parse database queries using inductive logic programming
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ATHENA: an ontology-driven system for natural language querying over relational data stores
Diptikalyan Saha, Avrilia Floratou, Karthik Sankaranarayanan, Umar Farooq Minhas, Ashish R. Mittal, and Fatma Özcan. 2016 · 2016
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 R. Radev. 2018 · 2018
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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, and Peter J. Liu. 2020 · 2020
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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
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Semantic evaluation for text-to-sql with distilled test suite
Ruiqi Zhong, Tao Yu, and Dan Klein. 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.
RYANSQL: recursively applying sketch-based slot fillings for complex text-to-sql in cross-domain databases
DongHyun Choi, Myeongcheol Shin, EungGyun Kim, and Dong Ryeol Shin. 2021 · 2021
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PICARD: parsing incrementally for constrained auto-regressive decoding from language models
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Teaching models to express their uncertainty in words
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022 · 2022
Evaluating the text-to-sql capabilities of large language models
Nitarshan Rajkumar, Raymond Li, and Dzmitry Bahdanau. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022 · 2022
Later among the works it cites.
Few-shot text-to-sql translation using structure and content prompt learning
Zihui Gu, Ju Fan, Nan Tang, Lei Cao, Bowen Jia, Sam Madden, and Xiaoyong Du. 2023 · 2023
Closest in time.
A comprehensive evaluation of chatgpt’s zero-shot text-to-sql capability
Aiwei Liu, Xuming Hu, Lijie Wen, and Philip S Yu. 2023 · 2023
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Din-sql: Decomposed in-context learning of text-to-sql with self-correction
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Cited alongside, same era.
What makes good in-context examples for gpt-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2022 · 2022
Cited alongside, same era.
Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
Resdsql: Decoupling the skeleton parsing and schema linking for text-to-sql
Haoyang Li, Jing Zhang, Cuiping Li, and Hong Chen. 2023a
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. 2023b
Cited in the paper.
Mohammadreza Pourreza and Davood Rafiei. 2023 · 2023
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Sql-palm: Improved large language modeladaptation for text-to-sql
Ruoxi Sun, Sercan O Arik, Hootan Nakhost, Hanjun Dai, Rajarishi Sinha, Pengcheng Yin, and Tomas Pfister. 2023 · 2023
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023 · 2023
Closest in time.