{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T14:11:54Z","timestamp":1780495914347,"version":"3.54.1"},"reference-count":147,"publisher":"Association for Computing Machinery (ACM)","issue":"2","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>With the development of the Large Language Models (LLMs), a large range of LLM-based Text-to-SQL(Text2SQL) methods have emerged. This survey provides a comprehensive review of LLM-based Text2SQL studies. We first enumerate classic benchmarks and evaluation metrics. For the two mainstream methods, prompt engineering and finetuning, we introduce a comprehensive taxonomy and offer practical insights into each subcategory. We present an overall analysis of the above methods and various models evaluated on well-known datasets and extract some characteristics. Finally, we discuss the challenges and future directions in this field.<\/jats:p>","DOI":"10.1145\/3737873","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T07:29:56Z","timestamp":1748935796000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":41,"title":["A Survey on Employing Large Language Models for Text-to-SQL Tasks"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3726-0299","authenticated-orcid":false,"given":"Liang","family":"Shi","sequence":"first","affiliation":[{"name":"School of Computer Science, Peking University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9299-2662","authenticated-orcid":false,"given":"Zhengju","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Peking University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3678-5059","authenticated-orcid":false,"given":"Nan","family":"Zhang","sequence":"additional","affiliation":[{"name":"ZettaData US","place":["Bellevue, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2725-2927","authenticated-orcid":false,"given":"Xiaotong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Bytedance Technology Co Ltd","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8219-4499","authenticated-orcid":false,"given":"Zhi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Peking University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,9,10]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat et\u00a0al. 2023. 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Retrieved from https:\/\/arxiv.org\/abs\/2208.13629"},{"key":"e_1_3_1_90_2","unstructured":"Nitarshan Rajkumar Raymond Li and Dzmitry Bahdanau. 2022. Evaluating the Text-to-SQL capabilities of large language models. arXiv:2204.00498. Retrieved from https:\/\/arxiv.org\/abs\/2204.00498"},{"key":"e_1_3_1_91_2","unstructured":"Tonghui Ren Yuankai Fan Zhenying He Ren Huang Jiaqi Dai Can Huang Yinan Jing Kai Zhang Yifan Yang and X. Sean Wang. 2024. PURPLE: Making a Large Language Model a Better SQL Writer. arXiv:2403.20014. Retrieved from https:\/\/arxiv.org\/abs\/2403.20014"},{"key":"e_1_3_1_92_2","unstructured":"Baptiste Roziere Jonas Gehring Fabian Gloeckle Sten Sootla Itai Gat Xiaoqing Ellen Tan Yossi Adi Jingyu Liu Tal Remez J\u00e9r\u00e9my Rapin et\u00a0al. 2023. Code llama: Open foundation models for code. arXiv:2308.12950. Retrieved from https:\/\/arxiv.org\/abs\/2308.12950"},{"key":"e_1_3_1_93_2","doi-asserted-by":"crossref","unstructured":"Ohad Rubin and Jonathan Berant. 2020. SmBoP: Semi-autoregressive bottom-up semantic parsing. arXiv:2010.12412. Retrieved from https:\/\/arxiv.org\/abs\/2010.12412","DOI":"10.18653\/v1\/2021.naacl-main.29"},{"key":"e_1_3_1_94_2","doi-asserted-by":"crossref","unstructured":"Torsten Scholak Nathan Schucher and Dzmitry Bahdanau. 2021. PICARD: Parsing incrementally for constrained auto-regressive decoding from language models. arXiv:2109.05093. Retrieved from https:\/\/arxiv.org\/abs\/2109.05093","DOI":"10.18653\/v1\/2021.emnlp-main.779"},{"key":"e_1_3_1_95_2","doi-asserted-by":"publisher","DOI":"10.14778\/3407790.3407858"},{"key":"e_1_3_1_96_2","unstructured":"SenseTime. 2024. SenseChat. Retrieved from https:\/\/platform.sensenova.cn\/#\/doc?path=\/chat\/ChatCompletions\/ChatCompletions.md"},{"key":"e_1_3_1_97_2","unstructured":"Burr Settles. 2009. Active learning literature survey. (2009)."},{"key":"e_1_3_1_98_2","unstructured":"Lei Sheng Shuai-Shuai Xu and Wei Xie. 2025. 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Retrieved from https:\/\/arxiv.org\/abs\/2306.00739"},{"key":"e_1_3_1_102_2","article-title":"Sequence to sequence learning with neural networks","volume":"27","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems 27 (2014).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_103_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.327"},{"key":"e_1_3_1_104_2","unstructured":"Shayan Talaei Mohammadreza Pourreza Yu-Chen Chang Azalia Mirhoseini and Amin Saberi. 2024. CHESS: Contextual Harnessing for Efficient SQL Synthesis. arXiv:2405.16755. Retrieved from https:\/\/arxiv.org\/abs\/2405.16755"},{"key":"e_1_3_1_105_2","unstructured":"Rohan Taori Ishaan Gulrajani Tianyi Zhang Yann Dubois Xuechen Li Carlos Guestrin Percy Liang and Tatsunori B. Hashimoto. 2023. 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Retrieved from https:\/\/arxiv.org\/abs\/2412.15115"},{"key":"e_1_3_1_126_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-68309-1_11"},{"key":"e_1_3_1_127_2","article-title":"Tree of thoughts: Deliberate problem solving with large language models","volume":"36","author":"Yao Shunyu","year":"2024","unstructured":"Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2024. Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems 36 (2024).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_128_2","unstructured":"Shunyu Yao Jeffrey Zhao Dian Yu Nan Du Izhak Shafran Karthik Narasimhan and Yuan Cao. 2022. React: Synergizing reasoning and acting in language models. arXiv:2210.03629. Retrieved from https:\/\/arxiv.org\/abs\/2210.03629"},{"key":"e_1_3_1_129_2","doi-asserted-by":"crossref","unstructured":"Tao Yu Rui Zhang He Yang Er Suyi Li Eric Xue Bo Pang Xi Victoria Lin Yi Chern Tan Tianze Shi Zihan Li et\u00a0al. 2019. Cosql: A conversational text-to-sql challenge towards cross-domain natural language interfaces to databases. arXiv:1909.05378. Retrieved from https:\/\/arxiv.org\/abs\/1909.05378","DOI":"10.18653\/v1\/D19-1204"},{"key":"e_1_3_1_130_2","doi-asserted-by":"crossref","unstructured":"Tao Yu Rui Zhang Kai Yang Michihiro Yasunaga Dongxu Wang Zifan Li James Ma Irene Li Qingning Yao Shanelle Roman et\u00a0al. 2018. Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task. arXiv:1809.08887. Retrieved from https:\/\/arxiv.org\/abs\/1809.08887","DOI":"10.18653\/v1\/D18-1425"},{"key":"e_1_3_1_131_2","doi-asserted-by":"crossref","unstructured":"Tao Yu Rui Zhang Michihiro Yasunaga Yi Chern Tan Xi Victoria Lin Suyi Li Heyang Er Irene Li Bo Pang Tao Chen et\u00a0al. 2019. Sparc: Cross-domain semantic parsing in context. arXiv:1906.02285. Retrieved from https:\/\/arxiv.org\/abs\/1906.02285","DOI":"10.18653\/v1\/P19-1443"},{"key":"e_1_3_1_132_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i24.34770"},{"key":"e_1_3_1_133_2","first-page":"1050","volume-title":"Proceedings of the National Conference on Artificial Intelligence","author":"Zelle John M.","year":"1996","unstructured":"John M. Zelle and Raymond J. Mooney. 1996. Learning to parse database queries using inductive logic programming. In Proceedings of the National Conference on Artificial Intelligence. 1050\u20131055."},{"key":"e_1_3_1_134_2","unstructured":"Bin Zhang Yuxiao Ye Guoqing Du Xiaoru Hu Zhishuai Li Sun Yang Chi Harold Liu Rui Zhao Ziyue Li and Hangyu Mao. 2024. Benchmarking the Text-to-SQL capability of large language models: A comprehensive evaluation. arXiv:2403.02951. Retrieved from https:\/\/arxiv.org\/abs\/2403.02951"},{"key":"e_1_3_1_135_2","doi-asserted-by":"crossref","unstructured":"Chao Zhang Yuren Mao Yijiang Fan Yu Mi Yunjun Gao Lu Chen Dongfang Lou and Jinshu Lin. 2024. FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis. arXiv:2401.10506. Retrieved from https:\/\/arxiv.org\/abs\/2401.10506","DOI":"10.1145\/3626246.3653375"},{"key":"e_1_3_1_136_2","doi-asserted-by":"crossref","unstructured":"Hanchong Zhang Ruisheng Cao Lu Chen Hongshen Xu and Kai Yu. 2023. Act-sql: In-context learning for text-to-sql with automatically-generated chain-of-thought. arXiv:2310.17342. Retrieved from https:\/\/arxiv.org\/abs\/2310.17342","DOI":"10.18653\/v1\/2023.findings-emnlp.227"},{"key":"e_1_3_1_137_2","unstructured":"Hanchong Zhang Ruisheng Cao Hongshen Xu Lu Chen and Kai Yu. 2024. CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions. arXiv:2405.02712. Retrieved from https:\/\/arxiv.org\/abs\/2405.02712"},{"key":"e_1_3_1_138_2","unstructured":"Qinggang Zhang Junnan Dong Hao Chen Wentao Li Feiran Huang and Xiao Huang. 2024. Structure Guided Large Language Model for SQL Generation. arXiv:2402.13284. Retrieved from https:\/\/arxiv.org\/abs\/2402.13284"},{"key":"e_1_3_1_139_2","unstructured":"Tingkai Zhang Chaoyu Chen Cong Liao Jun Wang Xudong Zhao Hang Yu Jianchao Wang Jianguo Li and Wenhui Shi. 2024. SQLfuse: Enhancing Text-to-SQL Performance through Comprehensive LLM Synergy. arXiv:2407.14568. Retrieved from https:\/\/arxiv.org\/abs\/2407.14568"},{"key":"e_1_3_1_140_2","article-title":"Natural language interfaces for tabular data querying and visualization: A survey","author":"Zhang Weixu","year":"2024","unstructured":"Weixu Zhang, Yifei Wang, Yuanfeng Song, Victor Junqiu Wei, Yuxing Tian, Yiyan Qi, Jonathan H Chan, Raymond Chi-Wing Wong, and Haiqin Yang. 2024. Natural language interfaces for tabular data querying and visualization: A survey. IEEE Transactions on Knowledge and Data Engineering (2024).","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_141_2","doi-asserted-by":"crossref","unstructured":"Yi Zhang Jan Deriu George Katsogiannis-Meimarakis Catherine Kosten Georgia Koutrika and Kurt Stockinger. 2023. ScienceBenchmark: A Complex Real-World Benchmark for Evaluating Natural Language to SQL Systems. arXiv:2306.04743. 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Retrieved from https:\/\/arxiv.org\/abs\/2406.11434"},{"key":"e_1_3_1_148_2","unstructured":"Xiaohu Zhu Qian Li Lizhen Cui and Yongkang Liu. 2024. Large language model enhanced text-to-sql generation: A survey. arXiv:2410.06011. 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