{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T16:04:01Z","timestamp":1776096241628,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Front. Comput. Sci."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s11704-025-41136-3","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T04:28:39Z","timestamp":1761625719000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["SEA-SQL: semantic-enhanced text-to-SQL with adaptive refinement"],"prefix":"10.1007","volume":"20","author":[{"given":"Chaofan","family":"Li","sequence":"first","affiliation":[]},{"given":"Yingxia","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Yawen","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zheng","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,28]]},"reference":[{"key":"41136_CR1","first-page":"3977","volume-title":"Proceedings of the 27th International Joint Conference on Artificial Intelligence","author":"R Cai","year":"2018","unstructured":"Cai R, Xu B, Zhang Z, Yang X, Li Z, Liang Z. An encoder-decoder framework translating natural language to database queries. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2018, 3977\u20133983"},{"key":"41136_CR2","doi-asserted-by":"publisher","first-page":"150","DOI":"10.18653\/v1\/2023.acl-short.15","volume-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","author":"D Rai","year":"2023","unstructured":"Rai D, Wang B, Zhou Y, Yao Z. Improving generalization in language model-based text-to-SQL semantic parsing: two simple semantic boundary-based techniques. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2023, 150\u2013160"},{"key":"41136_CR3","first-page":"13067","volume-title":"Proceedings of the 37th AAAI Conference on Artificial Intelligence","author":"H Li","year":"2023","unstructured":"Li H, Zhang J, Li C, Chen H. RESDSQL: decoupling schema linking and skeleton parsing for text-to-SQL. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence. 2023, 13067\u201313075"},{"key":"41136_CR4","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1109\/SLT54892.2023.10023434","volume-title":"Proceedings of 2022 IEEE Spoken Language Technology Workshop (SLT)","author":"L Zeng","year":"2023","unstructured":"Zeng L, Parthasarathi S H K, Hakkani-Tur D. N-best hypotheses reranking for text-to-SQL systems. In: Proceedings of 2022 IEEE Spoken Language Technology Workshop (SLT). 2023, 663\u2013670"},{"key":"41136_CR5","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.18653\/v1\/2021.findings-acl.100","volume-title":"Proceedings of Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021","author":"Q Liu","year":"2021","unstructured":"Liu Q, Yang D, Zhang J, Guo J, Zhou B, Lou J G. Awakening latent grounding from pretrained language models for semantic parsing. In: Proceedings of Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. 2021, 1174\u20131189"},{"key":"41136_CR6","unstructured":"Hui B, Shi X, Geng R, Li B, Li Y, Sun J, Zhu X. Improving text-to-SQL with schema dependency learning. 2021, arXiv preprint arXiv: 2103.04399"},{"key":"41136_CR7","doi-asserted-by":"crossref","unstructured":"Yu T, Yasunaga M, Yang K, Zhang R, Wang D, Li Z, Radev D. SyntaxSQLNet: syntax tree networks for complex and cross-DomainText-to-SQL task. 2018, arXiv preprint arXiv: 1810.05237","DOI":"10.18653\/v1\/D18-1193"},{"key":"41136_CR8","doi-asserted-by":"publisher","first-page":"7567","DOI":"10.18653\/v1\/2020.acl-main.677","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"B Wang","year":"2020","unstructured":"Wang B, Shin R, Liu X, Polozov O, Richardson M. RAT-SQL: relation-aware schema encoding and linking for text-to-SQL parsers. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020, 7567\u20137578"},{"key":"41136_CR9","doi-asserted-by":"publisher","first-page":"2030","DOI":"10.18653\/v1\/2021.findings-emnlp.174","volume-title":"Proceedings of Findings of the Association for Computational Linguistics: EMNLP 2021","author":"Y Gan","year":"2021","unstructured":"Gan Y, Chen X, Xie J, Purver M, Woodward J R, Drake J, Zhang Q. Natural SQL: making SQL easier to infer from natural language specifications. In: Proceedings of Findings of the Association for Computational Linguistics: EMNLP 2021. 2021, 2030\u20132042"},{"key":"41136_CR10","doi-asserted-by":"publisher","first-page":"440","DOI":"10.18653\/v1\/P17-1041","volume-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"P Yin","year":"2017","unstructured":"Yin P, Neubig G. A syntactic neural model for general-purpose code generation. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017, 440\u2013450"},{"key":"41136_CR11","doi-asserted-by":"publisher","first-page":"9895","DOI":"10.18653\/v1\/2021.emnlp-main.779","volume-title":"Proceedings of 2021 Conference on Empirical Methods in Natural Language Processing","author":"T Scholak","year":"2021","unstructured":"Scholak T, Schucher N, Bahdanau D. PICARD: parsing incrementally for constrained auto-regressive decoding from language models. In: Proceedings of 2021 Conference on Empirical Methods in Natural Language Processing. 2021, 9895\u20139901"},{"key":"41136_CR12","doi-asserted-by":"publisher","first-page":"8372","DOI":"10.18653\/v1\/2020.acl-main.742","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"A Suhr","year":"2020","unstructured":"Suhr A, Chang M W, Shaw P, Lee K. Exploring unexplored generalization challenges for cross-database semantic parsing. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020, 8372\u20138388"},{"key":"41136_CR13","first-page":"1835","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","author":"J Li","year":"2023","unstructured":"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 C C, Huang F, Cheng R, Li Y. Can LLM already serve as A database interface? A big bench for large-scale database grounded text-to-SQLs. In: Proceedings of the 37th International Conference on Neural Information Processing Systems. 2023, 1835"},{"key":"41136_CR14","unstructured":"Liu A, Hu X, Wen L, Yu P S. A comprehensive evaluation of ChatGPT\u2019s zero-shot text-to-SQL capability. 2023, arXiv preprint arXiv: 2303.13547"},{"key":"41136_CR15","unstructured":"Chang S, Fosler-Lussier E. How to prompt LLMS for text-to-SQL: a study in zero-shot, single-domain, and cross-domain settings. 2023, arXiv preprint arXiv: 2305.11853"},{"key":"41136_CR16","unstructured":"Dong X, Zhang C, Ge Y, Mao Y, Gao Y, Chen L, Lin J, Lou D. C3: zero-shot text-to-SQL with ChatGPT. 2023, arXiv preprint arXiv: 2307.07306"},{"key":"41136_CR17","first-page":"1577","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","author":"M Pourreza","year":"2023","unstructured":"Pourreza M, Rafiei D. DIN-SQL: decomposed in-context learning of text-to-SQL with self-correction. In: Proceedings of the 37th International Conference on Neural Information Processing Systems. 2023, 1577"},{"issue":"5","key":"41136_CR18","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.14778\/3641204.3641221","volume":"17","author":"D Gao","year":"2024","unstructured":"Gao D, Wang H, Li Y, Sun X, Qian Y, Ding B, Zhou J. Text-to-SQL empowered by large language models: a benchmark evaluation. Proceedings of the VLDB Endowment, 2024, 17(5): 1132\u20131145","journal-title":"Proceedings of the VLDB Endowment"},{"issue":"1","key":"41136_CR19","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/s41019-023-00235-6","volume":"9","author":"X Zhou","year":"2024","unstructured":"Zhou X, Sun Z, Li G. DB-GPT: large language model meets database. Data Science and Engineering, 2024, 9(1): 102\u2013111","journal-title":"Data Science and Engineering"},{"key":"41136_CR20","first-page":"1","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"S Yao","year":"2023","unstructured":"Yao S, Zhao J, Yu D, Du N, Shafran I, Narasimhan K R, Cao Y. ReAct: synergizing reasoning and acting in language models. In: Proceedings of the 11th International Conference on Learning Representations. 2023, 1\u201333"},{"key":"41136_CR21","unstructured":"Wang B, Ren C, Yang J, Liang X, Bai J, Zhang Q W, Yan Z, Li Z. MAC-SQL: a multi-agent collaborative framework for text-to-SQL. 2023, arXiv preprint arXiv: 2312.11242"},{"key":"41136_CR22","first-page":"1","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"X Wang","year":"2023","unstructured":"Wang X, Wei J, Schuurmans D, Le Q V, Chi E H, Narang S, Chowdhery A, Zhou D. Self-consistency improves chain of thought reasoning in language models. In: Proceedings of the 11th International Conference on Learning Representations. 2023, 1\u201324"},{"key":"41136_CR23","doi-asserted-by":"publisher","first-page":"4870","DOI":"10.18653\/v1\/2020.findings-emnlp.438","volume-title":"Proceedings of Findings of the Association for Computational Linguistics: EMNLP 2020","author":"X V Lin","year":"2020","unstructured":"Lin X V, Socher R, Xiong C. Bridging textual and tabular data for cross-domain text-to-SQL semantic parsing. In: Proceedings of Findings of the Association for Computational Linguistics: EMNLP 2020. 2020, 4870\u20134888"},{"issue":"2","key":"41136_CR24","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/s41019-024-00241-2","volume":"9","author":"X Wan","year":"2024","unstructured":"Wan X, Han X. Efficient top-k frequent itemset mining on massive data. Data Science and Engineering, 2024, 9(2): 177\u2013203","journal-title":"Data Science and Engineering"},{"issue":"3","key":"41136_CR25","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1145\/3654930","volume":"2","author":"H Li","year":"2024","unstructured":"Li H, Zhang J, Liu H, Fan J, Zhang X, Zhu J, Wei R, Pan H, Li C, Chen H. CodeS: towards building open-source language models for text-to-SQL. Proceedings of the ACM on Management of Data, 2024, 2(3): 127","journal-title":"Proceedings of the ACM on Management of Data"},{"issue":"4","key":"41136_CR26","doi-asserted-by":"publisher","first-page":"174609","DOI":"10.1007\/s11704-022-2041-5","volume":"17","author":"J Wang","year":"2023","unstructured":"Wang J, Zhao W, Tu X, He T. A novel dense retrieval framework for long document retrieval. Frontiers of Computer Science, 2023, 17(4): 174609","journal-title":"Frontiers of Computer Science"},{"key":"41136_CR27","first-page":"1800","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems","author":"J Wei","year":"2024","unstructured":"Wei J, Wang X, Schuurmans D, Bosma M, Ichter B, Xia F, Chi E H, Le Q V, Zhou D. Chain-of-thought prompting elicits reasoning in large language models. In: Proceedings of the 36th International Conference on Neural Information Processing Systems. 2024, 1800"},{"key":"41136_CR28","doi-asserted-by":"publisher","first-page":"3911","DOI":"10.18653\/v1\/D18-1425","volume-title":"Proceedings of 2018 Conference on Empirical Methods in Natural Language Processing","author":"T Yu","year":"2018","unstructured":"Yu T, Zhang R, Yang K, Yasunaga M, Wang D, Li Z, Ma J, Li I, Yao Q, Roman S, Zhang Z, Radev D R. Spider: a large-scale humanlabeled dataset for complex and cross-domain semantic parsing and text-to-SQL task. In: Proceedings of 2018 Conference on Empirical Methods in Natural Language Processing. 2018, 3911\u20133921"},{"key":"41136_CR29","first-page":"1337","volume-title":"Proceedings of 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"X Deng","year":"2021","unstructured":"Deng X, Awadallah A H, Meek C, Polozov O, Sun H, Richardson M. Structure-grounded pretraining for text-to-SQL. In: Proceedings of 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021, 1337\u20131350"},{"key":"41136_CR30","doi-asserted-by":"publisher","first-page":"396","DOI":"10.18653\/v1\/2020.emnlp-main.29","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"R Zhong","year":"2020","unstructured":"Zhong R, Yu T, Klein D. Semantic evaluation for text-to-SQL with distilled test suites. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020, 396\u2013411"},{"key":"41136_CR31","volume-title":"Proceedings of the 12th International Conference on Learning Representations","author":"Y Xu","year":"2024","unstructured":"Xu Y, Xie L, Gu X, Chen X, Chang H, Zhang H, Chen Z, Zhang X, Tian Q. QA-LoRA: quantization-aware low-rank adaptation of large language models. In: Proceedings of the 12th International Conference on Learning Representations, 2024"},{"key":"41136_CR32","first-page":"1","volume-title":"Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis","author":"R Y Aminabadi","year":"2022","unstructured":"Aminabadi R Y, Rajbhandari S, Awan A A, Li C, Li D, Zheng E, Ruwase O, Smith S, Zhang M, Rasley J, He Y. DeepSpeed- inference: enabling efficient inference of transformer models at unprecedented scale. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 2022, 1\u201315"},{"key":"41136_CR33","first-page":"1189","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems","author":"T Dao","year":"2022","unstructured":"Dao T, Fu D Y, Ermon S, Rudra A, R\u00e9 C. FLASHATTENTION: fast and memory-efficient exact attention with IO-awareness. In: Proceedings of the 36th International Conference on Neural Information Processing Systems. 2022, 1189"},{"issue":"3","key":"41136_CR34","doi-asserted-by":"publisher","first-page":"183609","DOI":"10.1007\/s11704-023-3626-3","volume":"18","author":"W Jiang","year":"2024","unstructured":"Jiang W, Ning B, Li G, Bai M, Jia X, Wei F. Graph-decomposed k-NN searching algorithm on road network. Frontiers of Computer Science, 2024, 18(3): 183609","journal-title":"Frontiers of Computer Science"},{"issue":"5","key":"41136_CR35","doi-asserted-by":"publisher","first-page":"185606","DOI":"10.1007\/s11704-023-3344-x","volume":"18","author":"P Jin","year":"2024","unstructured":"Jin P, Chu Z, Liu G, Luo Y, Wan S. Optimizing B+-tree for hybrid memory with in-node hotspot cache and eADR awareness. Frontiers of Computer Science, 2024, 18(5): 185606","journal-title":"Frontiers of Computer Science"},{"issue":"6","key":"41136_CR36","doi-asserted-by":"publisher","first-page":"176616","DOI":"10.1007\/s11704-023-3460-7","volume":"17","author":"F Li","year":"2023","unstructured":"Li F, Zhang T, Cui S, Liu H, Ren Z, Di D, Wang X, Zhang P, Yu G. A sampling method based on forecasting and combinatorial optimization for high performance A\/B testing. Frontiers of Computer Science, 2023, 17(6): 176616","journal-title":"Frontiers of Computer Science"},{"key":"41136_CR37","unstructured":"Yang A, Yang B, Hui B, Zheng B, Yu B, Zhou C, Li C, Li C, Liu D, Huang F, Dong G, Wei H, Lin H, Tang J, Wang J, Yang J, Tu J, Zhang J, Ma J, Yang J, Xu J, Zhou J, Bai J, He J, Lin J, Dang K, Lu K, Chen K, Yang K, Li M, Xue M, Ni N, Zhang P, Wang P, Peng R, Men R, Gao R, Lin R, Wang S, Bai S, Tan S, Zhu T, Li T, Liu T, Ge W, Deng X, Zhou X, Ren X, Zhang X, Wei X, Ren X, Liu X, Fan Y, Yao Y, Zhang Y, Wan Y, Chu Y, Liu Y, Cui Z, Zhang Z, Guo Z, Fan Z. Qwen2 technical report. 2024, arXiv preprint arXiv: 2407. 1067, 1: 2024"},{"key":"41136_CR38","unstructured":"Dubey A, Jauhri A, Pandey A, Kadian A, Al-Dahle A, et al. The Llama 3 herd of models. 2024, arXiv preprint arXiv: 2407.21783"},{"key":"41136_CR39","unstructured":"Rivi\u00e8re M, Pathak S, Sessa P G, Hardin C, Bhupatiraju S, et al. Gemma 2: improving open language models at a practical size. 2024, arXiv preprint arXiv: 2408.00118"},{"key":"41136_CR40","unstructured":"Touvron H, Martin L, Stone K, Albert P, Almahairi A, Babaei Y, Bashlykov N, Batra S, Bhargava P, Bhosale S, Bikel D, Blecher L, Canton Ferrer C, Chen M, Cucurull G, Esiobu D, Fernandes J, Fu J, Fu W, Fuller B, Gao C, Goswami V, Goyal N, Hartshorn A, Hosseini S, Hou R, Inan H, Kardas M, Kerkez V, Khabsa M, Kloumann I, Korenev A, Koura P S, Lachaux M A, Lavril T, Lee J, Liskovich D, Lu Y, Mao Y, Martinet X, Mihaylov T, Mishra P, Molybog I, Nie Y, Poulton A, Reizenstein J, Rungta R, Saladi K, Schelten A, Silva R, Smith E M, Subramanian R, Tan X E, Tang B, Taylor R, Williams A, Kuan J X, Xu P, Yan Z, Zarov I, Zhang Y, Fan A, Kambadur M, Narang S, Rodriguez A, Stojnic R, Edunov S, Scialom T. Llama 2: open foundation and fine-tuned chat models. 2023, arXiv preprint arXiv: 2307.09288"},{"key":"41136_CR41","first-page":"1","volume-title":"Proceedings of Workshops at the 50th International Conference on Very Large Data Bases","author":"H Zhang","year":"2024","unstructured":"Zhang H, Dong Y, Xiao C, Oyamada M. Large language models as data preprocessors. In: Proceedings of Workshops at the 50th International Conference on Very Large Data Bases. 2024, 1\u20134"}],"container-title":["Frontiers of Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-025-41136-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11704-025-41136-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11704-025-41136-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T04:28:44Z","timestamp":1761625724000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11704-025-41136-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,28]]},"references-count":41,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["41136"],"URL":"https:\/\/doi.org\/10.1007\/s11704-025-41136-3","relation":{},"ISSN":["2095-2228","2095-2236"],"issn-type":[{"value":"2095-2228","type":"print"},{"value":"2095-2236","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,28]]},"assertion":[{"value":"22 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests or financial conflicts to disclose.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"2003602"}}