Built on

J. M. Zelle and R. J. Mooney, “Learning to parse database queries using inductive logic programming,” in Proceedings of the national conference on artificial intelligence , 1996, pp. 1050–1055
1996
Earlier work this paper cites.
I. Sutskever, “Sequence to sequence learning with neural networks,” arXiv preprint arXiv:1409.3215 , 2014
Original
2014
Earlier work this paper cites.
A. Vaswani, “Attention is all you need,” Advances in Neural Information Processing Systems , 2017
2017
Earlier work this paper cites.
T. Yu, R. Zhang, K. Yang, M. Yasunaga, D. Wang, Z. Li, J. Ma, I. Li, Q. Yao, S. Roman et al. , “Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task,” arXiv preprint arXiv:1809.08887 , 2018
Original
2018
Earlier work this paper cites.
B. Wang, R. Shin, X. Liu, O. Polozov, and M. Richardson, “Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers,” arXiv preprint arXiv:1911.04942 , 2019
Original
2019
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J. Guo, Z. Zhan, Y. Gao, Y. Xiao, J.-G. Lou, T. Liu, and D. Zhang, “Towards complex text-to-sql in cross-domain database with intermediate representation,” arXiv preprint arXiv:1905.08205 , 2019
Original
2019
Earlier work this paper cites.
B. Bogin, M. Gardner, and J. Berant, “Representing schema structure with graph neural networks for text-to-sql parsing,” arXiv preprint arXiv:1905.06241 , 2019
Original
2019
Earlier work this paper cites.
W. Lei, W. Wang, Z. Ma, T. Gan, W. Lu, M.-Y. Kan, and T.-S. Chua, “Re-examining the role of schema linking in text-to-sql,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2020, pp. 6943–6954
2020
Earlier work this paper cites.
T. Wolfson, D. Deutch, and J. Berant, “Weakly supervised text-to-sql parsing through question decomposition,” arXiv preprint arXiv:2112.06311 , 2021
Original
2021
Earlier work this paper cites.
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. D. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman et al. , “Evaluating large language models trained on code,” arXiv preprint arXiv:2107.03374 , 2021
Original
2021
Earlier work this paper cites.
Z. Chen, L. Chen, Y. Zhao, R. Cao, Z. Xu, S. Zhu, and K. Yu, “Shadowgnn: Graph projection neural network for text-to-sql parser,” arXiv preprint arXiv:2104.04689 , 2021
Original
2021
Earlier work this paper cites.
R. Cai, J. Yuan, B. Xu, and Z. Hao, “Sadga: Structure-aware dual graph aggregation network for text-to-sql,” Advances in Neural Information Processing Systems , vol. 34, pp. 7664–7676, 2021
2021
Earlier work this paper cites.
B. Qin, B. Hui, L. Wang, M. Yang, J. Li, B. Li, R. Geng, R. Cao, J. Sun, L. Si et al. , “A survey on text-to-sql parsing: Concepts, methods, and future directions,” arXiv preprint arXiv:2208.13629 , 2022
Original
2022
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J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems , vol. 35, pp. 24 824–24 837, 2022
2022
Earlier work this paper cites.

Similar

Y. Li, D. Choi, J. Chung, N. Kushman, J. Schrittwieser, R. Leblond, T. Eccles, J. Keeling, F. Gimeno, A. Dal Lago et al. , “Competition-level code generation with alphacode,” Science , vol. 378, no. 6624, pp. 1092–1097, 2022
2022
Cited alongside, same era.
G. Katsogiannis-Meimarakis and G. Koutrika, “A survey on deep learning approaches for text-to-sql,” The VLDB Journal , vol. 32, no. 4, pp. 905–936, 2023
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Cited alongside, same era.
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2023
Cited alongside, same era.
D. Gao, H. Wang, Y. Li, X. Sun, Y. Qian, B. Ding, and J. Zhou, “Text-to-sql empowered by large language models: A benchmark evaluation,” arXiv preprint arXiv:2308.15363 , 2023
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2023
Cited alongside, same era.

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2024

RSL-SQL: Robust Schema Linking in Text-to-SQL Generation

Cao, Zhenbiao, Zheng, Yuanlei, Fan, Zhihao et al.

Understand

Text-to-SQL generation aims to translate natural language questions into SQL statements.

In Text-to-SQL based on large language models, schema linking is a widely adopted strategy to streamline the input for LLMs by selecting only relevant schema elements, therefore reducing noise and computational overhead.
However, schema linking faces risks that require caution, including the potential omission of necessary elements and disruption of database structural integrity.
To address these challenges, we propose a novel framework called RSL-SQL that combines bidirectional schema linking, contextual information augmentation, binary selection strategy, and multi-turn self-correction.

Built on

J. M. Zelle and R. J. Mooney, “Learning to parse database queries using inductive logic programming,” in Proceedings of the national conference on artificial intelligence , 1996, pp. 1050–1055
1996
Earlier work this paper cites.
I. Sutskever, “Sequence to sequence learning with neural networks,” arXiv preprint arXiv:1409.3215 , 2014
Original
2014
Earlier work this paper cites.
A. Vaswani, “Attention is all you need,” Advances in Neural Information Processing Systems , 2017
2017
Earlier work this paper cites.
T. Yu, R. Zhang, K. Yang, M. Yasunaga, D. Wang, Z. Li, J. Ma, I. Li, Q. Yao, S. Roman et al. , “Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task,” arXiv preprint arXiv:1809.08887 , 2018
Original
2018
Earlier work this paper cites.
B. Wang, R. Shin, X. Liu, O. Polozov, and M. Richardson, “Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers,” arXiv preprint arXiv:1911.04942 , 2019
Original
2019
Earlier work this paper cites.
J. Guo, Z. Zhan, Y. Gao, Y. Xiao, J.-G. Lou, T. Liu, and D. Zhang, “Towards complex text-to-sql in cross-domain database with intermediate representation,” arXiv preprint arXiv:1905.08205 , 2019
Original
2019
Earlier work this paper cites.
B. Bogin, M. Gardner, and J. Berant, “Representing schema structure with graph neural networks for text-to-sql parsing,” arXiv preprint arXiv:1905.06241 , 2019
Original
2019
Earlier work this paper cites.
W. Lei, W. Wang, Z. Ma, T. Gan, W. Lu, M.-Y. Kan, and T.-S. Chua, “Re-examining the role of schema linking in text-to-sql,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2020, pp. 6943–6954
2020
Earlier work this paper cites.
T. Wolfson, D. Deutch, and J. Berant, “Weakly supervised text-to-sql parsing through question decomposition,” arXiv preprint arXiv:2112.06311 , 2021
Original
2021
Earlier work this paper cites.
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. D. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman et al. , “Evaluating large language models trained on code,” arXiv preprint arXiv:2107.03374 , 2021
Original
2021
Earlier work this paper cites.
Z. Chen, L. Chen, Y. Zhao, R. Cao, Z. Xu, S. Zhu, and K. Yu, “Shadowgnn: Graph projection neural network for text-to-sql parser,” arXiv preprint arXiv:2104.04689 , 2021
Original
2021
Earlier work this paper cites.
R. Cai, J. Yuan, B. Xu, and Z. Hao, “Sadga: Structure-aware dual graph aggregation network for text-to-sql,” Advances in Neural Information Processing Systems , vol. 34, pp. 7664–7676, 2021
2021
Earlier work this paper cites.
B. Qin, B. Hui, L. Wang, M. Yang, J. Li, B. Li, R. Geng, R. Cao, J. Sun, L. Si et al. , “A survey on text-to-sql parsing: Concepts, methods, and future directions,” arXiv preprint arXiv:2208.13629 , 2022
Original
2022
Earlier work this paper cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems , vol. 35, pp. 24 824–24 837, 2022
2022
Earlier work this paper cites.

Similar

Y. Li, D. Choi, J. Chung, N. Kushman, J. Schrittwieser, R. Leblond, T. Eccles, J. Keeling, F. Gimeno, A. Dal Lago et al. , “Competition-level code generation with alphacode,” Science , vol. 378, no. 6624, pp. 1092–1097, 2022
2022
Cited alongside, same era.
G. Katsogiannis-Meimarakis and G. Koutrika, “A survey on deep learning approaches for text-to-sql,” The VLDB Journal , vol. 32, no. 4, pp. 905–936, 2023
2023
Cited alongside, same era.
J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat et al. , “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023
Original
2023
Cited alongside, same era.
D. Gao, H. Wang, Y. Li, X. Sun, Y. Qian, B. Ding, and J. Zhou, “Text-to-sql empowered by large language models: A benchmark evaluation,” arXiv preprint arXiv:2308.15363 , 2023
Original
2023
Cited alongside, same era.

Then

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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C.-Y. Tai, Z. Chen, T. Zhang, X. Deng, and H. Sun, “Exploring chain-of-thought style prompting for text-to-sql,” arXiv preprint arXiv:2305.14215 , 2023

Original

2023

Cited alongside, same era.

X. Dong, C. Zhang, Y. Ge, Y. Mao, Y. Gao, J. Lin, D. Lou et al. , “C3: Zero-shot text-to-sql with chatgpt,” arXiv preprint arXiv:2307.07306 , 2023

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2023

Cited alongside, same era.

A. Ni, S. Iyer, D. Radev, V. Stoyanov, W.-t. Yih, S. Wang, and X. V. Lin, “Lever: Learning to verify language-to-code generation with execution,” in International Conference on Machine Learning . PMLR, 2023, pp. 26 106–26 128

2023

Cited alongside, same era.

R. Sun, S. Ö. Arik, A. Muzio, L. Miculicich, S. Gundabathula, P. Yin, H. Dai, H. Nakhost, R. Sinha, Z. Wang et al. , “Sql-palm: Improved large language model adaptation for text-to-sql (extended),” arXiv preprint arXiv:2306.00739 , 2023

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2023

Cited alongside, same era.

B. Wang, C. Ren, J. Yang, X. Liang, J. Bai, Q.-W. Zhang, Z. Yan, and Z. Li, “Mac-sql: Multi-agent collaboration for text-to-sql,” arXiv preprint arXiv:2312.11242 , 2023

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OpenAI, “Hello gpt-4o,” 2024. [Online]. Available: https://openai.com/index/hello-gpt-4o/

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M. Pourreza and D. Rafiei, “Din-sql: Decomposed in-context learning of text-to-sql with self-correction,” Advances in Neural Information Processing Systems , vol. 36, 2024

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M. Pourreza and D. Rafiei, “Dts-sql: Decomposed text-to-sql with small large language models,” arXiv preprint arXiv:2402.01117 , 2024

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H. Li, J. Zhang, H. Liu, J. Fan, X. Zhang, J. Zhu, R. Wei, H. Pan, C. Li, and H. Chen, “Codes: Towards building open-source language models for text-to-sql,” Proceedings of the ACM on Management of Data , vol. 2, no. 3, pp. 1–28, 2024

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B. Li, Y. Luo, C. Chai, G. Li, and N. Tang, “The dawn of natural language to sql: Are we fully ready?” arXiv preprint arXiv:2406.01265 , 2024

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S. K. Gorti, I. Gofman, Z. Liu, J. Wu, N. Vouitsis, G. Yu, J. C. Cresswell, and R. Hosseinzadeh, “Msc-sql: Multi-sample critiquing small language models for text-to-sql translation,” arXiv preprint arXiv:2410.12916 , 2024

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K. Maamari, F. Abubaker, D. Jaroslawicz, and A. Mhedhbi, “The death of schema linking? text-to-sql in the age of well-reasoned language models,” arXiv preprint arXiv:2408.07702 , 2024

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