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Self-correction in text-to-SQL is the process of prompting large language model (LLM) to revise its previously incorrectly generated SQL, and commonly relies on manually crafted self-correction guidelines by human experts that are not only labor-intensive to produce but also limited by the human ability in identifying all potential error patterns in LLM responses.
Towards complex text-to-sql in cross-domain database with intermediate representation
Guo, J.; Zhan, Z.; Gao, Y.; Xiao, Y.; Lou, J.-G.; Liu, T.; and Zhang, D. 2019 · 1905
Earlier work this paper cites.
Learning to parse database queries using inductive logic programming
Zelle, J. M.; and Mooney, R. J. 1996 · 1996
Earlier work this paper cites.
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
Yu, T.; Zhang, R.; Yang, K.; Yasunaga, M.; Wang, D.; Li, Z.; Ma, J.; Li, I.; Yao, Q.; Roman, S.; Zhang, Z.; and Radev, D. 2018 · 2018
Earlier work this paper cites.
Evaluating the text-to-sql capabilities of large language models
Rajkumar, N.; Li, R.; and Bahdanau, D. 2022 · 2022
Earlier work this paper cites.
Self-Consistency Improves Chain of Thought Reasoning in Language Models
Wang, X.; Wei, J.; Schuurmans, D.; Le, Q. V.; Chi, E. H.; Narang, S.; Chowdhery, A.; and Zhou, D. 2022 · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Xia, F.; Chi, E.; Le, Q. V.; Zhou, D.; et al. 2022 · 2022
Earlier work this paper cites.
Teaching large language models to self-debug
Chen, X.; Lin, M.; Schärli, N.; and Zhou, D. 2023 · 2023
Earlier work this paper cites.
C3: Zero-shot text-to-sql with chatgpt
Dong, X.; Zhang, C.; Ge, Y.; Mao, Y.; Gao, Y.; Lin, J.; Lou, D.; et al. 2023 · 2023
Earlier work this paper cites.
Text-to-sql empowered by large language models: A benchmark evaluation
Gao, D.; Wang, H.; Li, Y.; Sun, X.; Qian, Y.; Ding, B.; and Zhou, J. 2023 · 2023
Earlier work this paper cites.
Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs
Li, J.; Hui, B.; QU, G.; Yang, J.; Li, B.; Li, B.; Wang, B.; Qin, B.; Geng, R.; Huo, N.; Zhou, X.; Ma, C.; Li, G.; Chang, K.; Huang, F.; Cheng, R.; and Li, Y. 2023 · 2023
Cited alongside, same era.
Self-refine: Iterative refinement with self-feedback
Madaan, A.; Tandon, N.; Gupta, P.; Hallinan, S.; Gao, L.; Wiegreffe, S.; Alon, U.; Dziri, N.; Prabhumoye, S.; Yang, Y.; et al. 2023 · 2023
Cited alongside, same era.
Pan, L.; Saxon, M.; Xu, W.; Nathani, D.; Wang, X.; and Wang, W. Y. 2023 · 2023
Cited alongside, same era.
DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction
Pourreza, M.; and Rafiei, D. 2023 · 2023
Cited alongside, same era.
Autogen: Enabling next-gen llm applications via multi-agent conversation framework
When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs
Kamoi, R.; Zhang, Y.; Zhang, N.; Han, J.; and Zhang, R. 2024 · 2024
Closest in time.
MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation
Lee, D.; Park, C.; Kim, J.; and Park, H. 2024 · 2024
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Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction Strategies
Pan, L.; Saxon, M.; Xu, W.; Nathani, D.; Wang, X.; and Wang, W. Y. 2024 · 2024
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DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models
Pourreza, M.; and Rafiei, D. 2024 · 2024
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Wu, Q.; Bansal, G.; Zhang, J.; Wu, Y.; Zhang, S.; Zhu, E.; Li, B.; Jiang, L.; Zhang, X.; and Wang, C. 2023 · 2023
Cited alongside, same era.
Openagents: An open platform for language agents in the wild
Xie, T.; Zhou, F.; Cheng, Z.; Shi, P.; Weng, L.; Liu, Y.; Hua, T. J.; Zhao, J.; Liu, Q.; Liu, C.; et al. 2023 · 2023
Cited alongside, same era.
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Zhou, D.; Schärli, N.; Hou, L.; Wei, J.; Scales, N.; Wang, X.; Schuurmans, D.; Cui, C.; Bousquet, O.; Le, Q. V.; et al. 2023 · 2023
Cited alongside, same era.
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
Gou, Z.; Shao, Z.; Gong, Y.; yelong shen; Yang, Y.; Duan, N.; and Chen, W. 2024 · 2024
Cited alongside, same era.
The Dawn of Natural Language to SQL: Are We Fully Ready?
Li, B.; Luo, Y.; Chai, C.; Li, G.; and Tang, N. 2024a
Cited in the paper.
CodeS: Towards Building Open-source Language Models for Text-to-SQL
Li, H.; Zhang, J.; Liu, H.; Fan, J.; Zhang, X.; Zhu, J.; Wei, R.; Pan, H.; Li, C.; and Chen, H. 2024b
Cited in the paper.
BIRD-SQL Leaderboard
Li, J.; Hui, B.; Qu, G.; Yang, J.; Li, B.; Li, B.; Wang, B.; Qin, B.; Cao, R.; Geng, R.; Huo, N.; Zhou, X.; Ma, C.; Li, G.; Chang, K. C. C.; Huang, F.; Cheng, R.; and Li, Y. 2024c
Cited in the paper.
Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls
Li, J.; Hui, B.; Qu, G.; Yang, J.; Li, B.; Li, B.; Wang, B.; Qin, B.; Geng, R.; Huo, N.; et al. 2024d
Cited in the paper.
Qu, G.; Li, J.; Li, B.; Qin, B.; Huo, N.; Ma, C.; and Cheng, R. 2024 · 2024
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Learning Performance-Improving Code Edits
Shypula, A. G.; Madaan, A.; Zeng, Y.; Alon, U.; Gardner, J. R.; Yang, Y.; Hashemi, M.; Neubig, G.; Ranganathan, P.; Bastani, O.; and Yazdanbakhsh, A. 2024 · 2024
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CHESS: Contextual Harnessing for Efficient SQL Synthesis
Talaei, S.; Pourreza, M.; Chang, Y.-C.; Mirhoseini, A.; and Saberi, A. 2024 · 2024
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Benchmarking the Text-to-SQL Capability of Large Language Models: A Comprehensive Evaluation
Zhang, B.; Ye, Y.; Du, G.; Hu, X.; Li, Z.; Yang, S.; Liu, C. H.; Zhao, R.; Li, Z.; and Mao, H. 2024 · 2024
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