{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T06:43:19Z","timestamp":1791960199162,"version":"4.3.4"},"reference-count":72,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:p>\n                    Translating users' natural language queries (nl) into sql queries (\n                    <jats:italic toggle=\"yes\">i.e.<\/jats:italic>\n                    , nl2sql) can significantly reduce barriers to accessing relational databases and support various commercial applications. The performance of nl2sql has been greatly improved with the emergence of large language models (LLMs). In this context, it is crucial to assess our current position, determine the nl2sql solutions that should be adopted for specific scenarios by practitioners, and identify the research topics that researchers should explore next.\n                  <\/jats:p>\n                  <jats:p>In this tutorial, we will provide a comprehensive overview of nl2sql techniques, covering every aspect of its lifecycle, from the collection and synthesis of training data, recent advancements in nl2sql translation techniques using LLMs and agents, debugging nl2sql processes, to multi-angle and scenario-based evaluation of nl2sql methods. We conclude by highlighting the research challenges and open problems in nl2sql.<\/jats:p>","DOI":"10.14778\/3750601.3750696","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:38:05Z","timestamp":1758029885000},"page":"5466-5471","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["Natural Language to SQL: State of the Art and Open Problems"],"prefix":"10.14778","volume":"18","author":[{"given":"Yuyu","family":"Luo","sequence":"first","affiliation":[{"name":"HKUST (GZ)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoliang","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ju","family":"Fan","sequence":"additional","affiliation":[{"name":"Renmin University of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengliang","family":"Chai","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Tang","sequence":"additional","affiliation":[{"name":"HKUST (GZ)"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Adithya Bhaskar Tushar Tomar Ashutosh Sathe and et al. 2023. Benchmarking and Improving Text-to-SQL Generation under Ambiguity. In EMNLP.","DOI":"10.18653\/v1\/2023.emnlp-main.436"},{"key":"e_1_2_1_2_1","volume-title":"ValueNet: A Natural Language-to-SQL System that Learns from Database Information","author":"Brunner Ursin","unstructured":"Ursin Brunner and Kurt Stockinger. 2021. ValueNet: A Natural Language-to-SQL System that Learns from Database Information. In ICDE. IEEE, 2177\u20132182."},{"key":"e_1_2_1_3_1","unstructured":"Hasan Alp Cafero\u011flu and \u00d6zg\u00fcr Ulusoy. 2024. E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL. arXiv:2409.16751 [cs.CL] https:\/\/arxiv.org\/abs\/2409.16751"},{"key":"e_1_2_1_4_1","unstructured":"Zhenbiao Cao Yuanlei Zheng Zhihao Fan Xiaojin Zhang Wei Chen and Xiang Bai. 2024. RSL-SQL: Robust Schema Linking in Text-to-SQL Generation. arXiv:2411.00073 [cs.CL] https:\/\/arxiv.org\/abs\/2411.00073"},{"key":"e_1_2_1_5_1","unstructured":"Shuaichen Chang Jun Wang Mingwen Dong Lin Pan and et al. 2023. Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness. In ICLR."},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Ziru Chen Shijie Chen Michael White Raymond J. Mooney and et al. 2023. Text-to-SQL Error Correction with Language Models of Code. In ACL.","DOI":"10.18653\/v1\/2023.acl-short.117"},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Yeounoh Chung Gaurav T. Kakkar Yu Gan Brenton Milne and Fatma Ozcan. 2025. Is Long Context All You Need? Leveraging LLM's Extended Context for NL2SQL. arXiv:2501.12372 [cs.DB] https:\/\/arxiv.org\/abs\/2501.12372","DOI":"10.14778\/3742728.3742761"},{"key":"e_1_2_1_8_1","volume-title":"Christopher Meek, and et al.","author":"Deng Xiang","year":"2021","unstructured":"Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek, and et al. 2021. Structure-Grounded Pretraining for Text-to-SQL. In NAACL-HLT."},{"key":"e_1_2_1_9_1","unstructured":"Xuemei Dong Chao Zhang Yuhang Ge and et al. 2023. C3: Zero-shot Text-to-SQL with ChatGPT. arXiv preprint arXiv:2307.07306 (2023)."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.910"},{"key":"e_1_2_1_11_1","volume-title":"Metasql: A Generate-then-Rank Framework for Natural Language to SQL Translation. arXiv:2402.17144 [cs.DB] https:\/\/arxiv.org\/abs\/2402.17144","author":"Fan Yuankai","year":"2024","unstructured":"Yuankai Fan, Zhenying He, Tonghui Ren, Can Huang, Yinan Jing, Kai Zhang, and X. Sean Wang. 2024. Metasql: A Generate-then-Rank Framework for Natural Language to SQL Translation. arXiv:2402.17144 [cs.DB] https:\/\/arxiv.org\/abs\/2402.17144"},{"key":"e_1_2_1_12_1","volume-title":"Radev","author":"Finegan-Dollak Catherine","year":"2018","unstructured":"Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, and Dragomir R. Radev. 2018. Improving Text-to-SQL Evaluation Methodology. In ACL (1). Association for Computational Linguistics, 351\u2013360."},{"key":"e_1_2_1_13_1","unstructured":"Avrilia Floratou Fotis Psallidas Fuheng Zhao Shaleen Deep Gunther Hagleither Wangda Tan and et al. 2024. NL2SQL is a solved problem... Not!. In CIDR."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.14778\/3583140.3583165"},{"key":"e_1_2_1_15_1","volume-title":"Natural SQL: Making SQL easier to infer from natural language specifications. arXiv preprint arXiv:2109.05153","author":"Gan Yujian","year":"2021","unstructured":"Yujian Gan, Xinyun Chen, Jinxia Xie, Matthew Purver, John R Woodward, John Drake, and Qiaofu Zhang. 2021. Natural SQL: Making SQL easier to infer from natural language specifications. arXiv preprint arXiv:2109.05153 (2021)."},{"key":"e_1_2_1_16_1","volume-title":"Proc. VLDB Endow.","author":"Gao Dawei","year":"2024","unstructured":"Dawei Gao, Haibin Wang, Yaliang Li, and et al. 2024. Text-to-sql empowered by large language models: A benchmark evaluation. Proc. VLDB Endow. (2024)."},{"key":"e_1_2_1_17_1","unstructured":"Zihui Gu and et al. 2023. Interleaving Pre-Trained Language Models and Large Language Models for Zero-Shot NL2SQL Generation. CoRR 2306.08891 (2023)."},{"key":"e_1_2_1_18_1","volume-title":"Proc. ACM Manag. Data","author":"Gu Zihui","year":"2023","unstructured":"Zihui Gu, Ju Fan, Nan Tang, and et al. 2023. Few-shot Text-to-SQL Translation using Structure and Content Prompt Learning. Proc. ACM Manag. Data (2023)."},{"key":"e_1_2_1_19_1","unstructured":"Yiqun Hu Yiyun Zhao Jiarong Jiang and et al. 2023. Importance of Synthesizing High-quality Data for Text-to-SQL Parsing. In ACL (Findings)."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.86"},{"key":"e_1_2_1_21_1","volume-title":"Data-Centric Text-to-SQL with Large Language Models. In NeurIPS 2024 Third Table Representation Learning Workshop. https:\/\/openreview.net\/forum?id=gDKIjZcg93","author":"Huang Zezhou","year":"2024","unstructured":"Zezhou Huang, Shuo Zhang, Kechen Liu, and Eugene Wu. 2024. Data-Centric Text-to-SQL with Large Language Models. In NeurIPS 2024 Third Table Representation Learning Workshop. https:\/\/openreview.net\/forum?id=gDKIjZcg93"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-acl.99"},{"key":"e_1_2_1_23_1","volume-title":"Proc. VLDB Endow.","author":"George","year":"2023","unstructured":"George Katsogiannis-Meimarakis and et al. 2023. Natural Language Interfaces for Databases with Deep Learning. Proc. VLDB Endow. (2023)."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457543"},{"key":"e_1_2_1_25_1","volume-title":"ACL\/IJCNLP","author":"Lee Chia-Hsuan","year":"2021","unstructured":"Chia-Hsuan Lee, Oleksandr Polozov, and Matthew Richardson. [n.d.]. KaggleD-BQA: Realistic Evaluation of Text-to-SQL Parsers. In ACL\/IJCNLP 2021."},{"key":"e_1_2_1_26_1","unstructured":"Dongjun Lee. 2019. Clause-Wise and Recursive Decoding for Complex and Cross-Domain Text-to-SQL Generation. arXiv:1904.08835 [cs.CL] https:\/\/arxiv.org\/abs\/1904.08835"},{"key":"e_1_2_1_27_1","unstructured":"Dongjun Lee Choongwon Park Jaehyuk Kim and Heesoo Park. 2024. MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation. arXiv:2405.07467 [cs.CL] https:\/\/arxiv.org\/abs\/2405.07467"},{"key":"e_1_2_1_28_1","unstructured":"Fangyu Lei Jixuan Chen Yuxiao Ye Ruisheng Cao Dongchan Shin Hongjin Su Zhaoqing Suo Hongcheng Gao Wenjing Hu Pengcheng Yin Victor Zhong Caiming Xiong Ruoxi Sun Qian Liu Sida Wang and Tao Yu. 2024. Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows. arXiv:2411.07763 [cs.CL] https:\/\/arxiv.org\/abs\/2411.07763"},{"key":"e_1_2_1_29_1","volume-title":"EMNLP (1)","author":"Lei Wenqiang","unstructured":"Wenqiang Lei, Weixin Wang, Zhixin Ma, Tian Gan, Wei Lu, Min-Yen Kan, and Tat-Seng Chua. 2020. Re-examining the Role of Schema Linking in Text-to-SQL. In EMNLP (1). Association for Computational Linguistics, 6943\u20136954."},{"key":"e_1_2_1_30_1","volume-title":"Proc. VLDB Endow.","author":"Li Boyan","year":"2024","unstructured":"Boyan Li, Yuyu Luo, and et al. 2024. The Dawn of Natural Language to SQL: Are We Fully Ready? Proc. VLDB Endow. (2024)."},{"key":"e_1_2_1_31_1","unstructured":"Boyan Li Jiayi Zhang Ju Fan Yanwei Xu Chong Chen Nan Tang and Yuyu Luo. 2025. Alpha-SQL: Zero-Shot Text-to-SQL using Monte Carlo Tree Search. arXiv:2502.17248 [cs.DB] https:\/\/arxiv.org\/abs\/2502.17248"},{"key":"e_1_2_1_32_1","unstructured":"Haoyang Li Shang Wu Xiaokang Zhang Xinmei Huang Jing Zhang Fuxin Jiang Shuai Wang Tieying Zhang Jianjun Chen Rui Shi Hong Chen and Cuiping Li. 2025. OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale. arXiv:2503.02240 [cs.CL] https:\/\/arxiv.org\/abs\/2503.02240"},{"key":"e_1_2_1_33_1","volume-title":"RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL. arXiv:2302.05965 [cs.CL]","author":"Li Haoyang","year":"2023","unstructured":"Haoyang Li, Jing Zhang, Cuiping Li, and Hong Chen. 2023. RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL. arXiv:2302.05965 [cs.CL]"},{"key":"e_1_2_1_34_1","unstructured":"Haoyang Li Jing Zhang Hanbing Liu Ju Fan Xiaokang Zhang Jun Zhu Renjie Wei Hongyan Pan Cuiping Li and Hong Chen. 2024. CodeS: Towards Building Open-source Language Models for Text-to-SQL. arXiv:2402.16347 [cs.CL] https:\/\/arxiv.org\/abs\/2402.16347"},{"key":"e_1_2_1_35_1","unstructured":"Jinyang Li and et al. 2023. Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs. CoRR (2023)."},{"key":"e_1_2_1_36_1","unstructured":"Jinyang Li and et al. 2023. Graphix-t5: Mixing pre-trained transformers with graph-aware layers for text-to-sql parsing. arXiv:2301.07507 (2023)."},{"key":"e_1_2_1_37_1","volume-title":"Natural Language Data Management and Interfaces: Recent Development and Open Challenges. In SIGMOD Conference.","author":"Li Yunyao","year":"2017","unstructured":"Yunyao Li and Davood Rafiei. 2017. Natural Language Data Management and Interfaces: Recent Development and Open Challenges. In SIGMOD Conference."},{"key":"e_1_2_1_38_1","unstructured":"Zhishuai Li Xiang Wang Jingjing Zhao Sun Yang Guoqing Du Xiaoru Hu Bin Zhang Yuxiao Ye Ziyue Li Rui Zhao and Hangyu Mao. 2024. PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency. arXiv:2403.09732 [cs.CL] https:\/\/arxiv.org\/abs\/2403.09732"},{"key":"e_1_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Aiwei Liu Xuming Hu Li Lin and Lijie Wen. 2022. Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph. In KDD.","DOI":"10.1145\/3534678.3539294"},{"key":"e_1_2_1_40_1","unstructured":"Xinyu Liu Shuyu Shen Boyan Li Peixian Ma Runzhi Jiang Yuxin Zhang Ju Fan Guoliang Li Nan Tang and Yuyu Luo. 2024. A Survey of NL2SQL with Large Language Models: Where are we and where are we going? arXiv:2408.05109 [cs.DB] https:\/\/arxiv.org\/abs\/2408.05109"},{"key":"e_1_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Xinyu Liu Shuyu Shen Boyan Li Nan Tang and Yuyu Luo. 2025. NL2SQL-BUGs: A Benchmark for Detecting Semantic Errors in NL2SQL Translation. arXiv:2503.11984 [cs.DB] https:\/\/arxiv.org\/abs\/2503.11984","DOI":"10.1145\/3711896.3737427"},{"key":"e_1_2_1_42_1","volume-title":"DeepEye: Towards Automatic Data Visualization","author":"Luo Yuyu","unstructured":"Yuyu Luo, Xuedi Qin, Nan Tang, and Guoliang Li. 2018. DeepEye: Towards Automatic Data Visualization. In ICDE. IEEE Computer Society, 101\u2013112."},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457261"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2021.3114848"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.14778\/3494124.3494139"},{"key":"e_1_2_1_46_1","volume-title":"The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models. CoRR abs\/2408.07702","author":"Maamari Karime","year":"2024","unstructured":"Karime Maamari, Fadhil Abubaker, Daniel Jaroslawicz, and Amine Mhedhbi. 2024. The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models. CoRR abs\/2408.07702 (2024)."},{"key":"e_1_2_1_47_1","doi-asserted-by":"crossref","unstructured":"Fatma \u0150zcan Abdul Quamar Jaydeep Sen and et al. 2020. State of the art and open challenges in natural language interfaces to data. In ACM SIGMOD.","DOI":"10.1145\/3318464.3383128"},{"key":"e_1_2_1_48_1","volume-title":"Yu Gan, Amin Saberi, Fatma Ozcan, and Sercan \u00d6. Arik.","author":"Pourreza Mohammadreza","year":"2024","unstructured":"Mohammadreza Pourreza, Hailong Li, Ruoxi Sun, Yeounoh Chung, Shayan Talaei, Gaurav Tarlok Kakkar, Yu Gan, Amin Saberi, Fatma Ozcan, and Sercan \u00d6. Arik. 2024. CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL. CoRR abs\/2410.01943 (2024)."},{"key":"e_1_2_1_49_1","volume-title":"Din-sql: Decomposed in-context learning of text-to-sql with self-correction. arXiv:2304.11015","author":"Pourreza Mohammadreza","year":"2023","unstructured":"Mohammadreza Pourreza and Davood Rafiei. 2023. Din-sql: Decomposed in-context learning of text-to-sql with self-correction. arXiv:2304.11015 (2023)."},{"key":"e_1_2_1_50_1","doi-asserted-by":"crossref","unstructured":"Mohammadreza Pourreza and Davood Rafiei. 2024. DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models. arXiv:2402.01117 [cs.CL] https:\/\/arxiv.org\/abs\/2402.01117","DOI":"10.18653\/v1\/2024.findings-emnlp.481"},{"key":"e_1_2_1_51_1","volume-title":"RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL. arXiv:2205.06983 [cs.CL] https:\/\/arxiv.org\/abs\/2205.06983","author":"Qi Jiexing","year":"2022","unstructured":"Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, and Zhouhan Lin. 2022. RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL. arXiv:2205.06983 [cs.CL] https:\/\/arxiv.org\/abs\/2205.06983"},{"key":"e_1_2_1_52_1","unstructured":"Nitarshan Rajkumar and et al. 2022. Evaluating the Text-to-SQL Capabilities of Large Language Models. CoRR abs\/2204.00498 (2022)."},{"key":"e_1_2_1_53_1","volume-title":"PURPLE: Making a Large Language Model a Better SQL Writer. arXiv:2403.20014 [cs.DB] https:\/\/arxiv.org\/abs\/2403.20014","author":"Ren Tonghui","year":"2024","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 [cs.DB] https:\/\/arxiv.org\/abs\/2403.20014"},{"key":"e_1_2_1_54_1","volume-title":"PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models. arXiv:2109.05093 [cs.CL]","author":"Scholak Torsten","year":"2021","unstructured":"Torsten Scholak and et al. 2021. PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models. arXiv:2109.05093 [cs.CL]"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.327"},{"key":"e_1_2_1_56_1","volume-title":"CHESS: Contextual Harnessing for Efficient SQL Synthesis. CoRR abs\/2405.16755","author":"Talaei Shayan","year":"2024","unstructured":"Shayan Talaei, Mohammadreza Pourreza, Yu-Chen Chang, Azalia Mirhoseini, and Amin Saberi. 2024. CHESS: Contextual Harnessing for Efficient SQL Synthesis. CoRR abs\/2405.16755 (2024)."},{"key":"e_1_2_1_57_1","volume-title":"Halevy","author":"Tang Nan","year":"2024","unstructured":"Nan Tang, Chenyu Yang, Ju Fan, Lei Cao, Yuyu Luo, and Alon Y. Halevy. 2024. VerifAI: Verified Generative AI. In CIDR. www.cidrdb.org."},{"key":"e_1_2_1_58_1","volume-title":"Hashimoto","author":"Taori Rohan","year":"2023","unstructured":"Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford Alpaca: An Instruction-following LLaMA model. https:\/\/github.com\/tatsu-lab\/stanford_alpaca."},{"key":"e_1_2_1_59_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar and et al. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_2_1_60_1","unstructured":"Bing Wang Changyu Ren Jian Yang and et al. 2023. MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL. CoRR abs\/2312.11242 (2023)."},{"key":"e_1_2_1_61_1","volume-title":"RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers","author":"Wang Bailin","unstructured":"Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, and Matthew Richardson. 2020. RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers. In ACL. Association for Computational Linguistics, 7567\u20137578."},{"key":"e_1_2_1_62_1","volume-title":"Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing. In KDD.","author":"Wang Lihan","year":"2022","unstructured":"Lihan Wang and et al. 2022. Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing. In KDD."},{"key":"e_1_2_1_63_1","volume-title":"Aakanksha Chowdhery, and Denny Zhou.","author":"Wang Xuezhi","year":"2023","unstructured":"Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023. Self-Consistency Improves Chain of Thought Reasoning in Language Models. arXiv:2203.11171 [cs.CL] https:\/\/arxiv.org\/abs\/2203.11171"},{"key":"e_1_2_1_64_1","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems","author":"Wei Jason","year":"2024","unstructured":"Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2024. Chain-of-thought prompting elicits reasoning in large language models. In Proceedings of the 36th International Conference on Neural Information Processing Systems (New Orleans, LA, USA) (NIPS '22). Curran Associates Inc., Red Hook, NY, USA, Article 1800, 14 pages."},{"key":"e_1_2_1_65_1","volume-title":"SIGMOD Conference.","author":"Weir Nathaniel","unstructured":"Nathaniel Weir, Prasetya Ajie Utama, Alex Galakatos, and et al. 2020. DBPal: A Fully Pluggable NL2SQL Training Pipeline. In SIGMOD Conference."},{"key":"e_1_2_1_66_1","volume-title":"Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task. In EMNLP.","author":"Yu Tao","year":"2018","unstructured":"Tao Yu and et al. 2018. Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task. In EMNLP."},{"key":"e_1_2_1_67_1","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 [cs.CL] https:\/\/arxiv.org\/abs\/2405.02712"},{"key":"e_1_2_1_68_1","volume-title":"AFlow: Automating Agentic Workflow Generation. CoRR abs\/2410.10762","author":"Zhang Jiayi","year":"2024","unstructured":"Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen, Jiaqi Chen, Mingchen Zhuge, Xin Cheng, Sirui Hong, Jinlin Wang, Bingnan Zheng, Bang Liu, Yuyu Luo, and Chenglin Wu. 2024. AFlow: Automating Agentic Workflow Generation. CoRR abs\/2410.10762 (2024)."},{"key":"e_1_2_1_69_1","doi-asserted-by":"crossref","unstructured":"Yi Zhang Jan Deriu and et al. 2023. ScienceBenchmark: A Complex Real-World Benchmark for Evaluating Natural Language to SQL Systems. VLDB (2023).","DOI":"10.14778\/3636218.3636225"},{"key":"e_1_2_1_70_1","volume-title":"Reward-SQL: Boosting Text-to-SQL via Stepwise Reasoning and Process-Supervised Rewards. CoRR abs\/2505.04671","author":"Zhang Yuxin","year":"2025","unstructured":"Yuxin Zhang, Meihao Fan, Ju Fan, Mingyang Yi, Yuyu Luo, Jian Tan, and Guoliang Li. 2025. Reward-SQL: Boosting Text-to-SQL via Stepwise Reasoning and Process-Supervised Rewards. CoRR abs\/2505.04671 (2025)."},{"key":"e_1_2_1_71_1","volume-title":"Are Large Language Models Good Statisticians? CoRR abs\/2406.07815","author":"Zhu Yizhang","year":"2024","unstructured":"Yizhang Zhu, Shiyin Du, Boyan Li, Yuyu Luo, and Nan Tang. 2024. Are Large Language Models Good Statisticians? CoRR abs\/2406.07815 (2024)."},{"key":"e_1_2_1_72_1","unstructured":"Yizhang Zhu Runzhi Jiang Boyan Li Nan Tang and Yuyu Luo. 2025. EllieSQL: Cost-Efficient Text-to-SQL with Complexity-Aware Routing. arXiv:2503.22402 [cs.DB] https:\/\/arxiv.org\/abs\/2503.22402"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3750601.3750696","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:39:48Z","timestamp":1758029988000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3750601.3750696"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":72,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["10.14778\/3750601.3750696"],"URL":"https:\/\/doi.org\/10.14778\/3750601.3750696","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2025,8]]},"assertion":[{"value":"2025-09-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}