{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T21:03:59Z","timestamp":1775595839229,"version":"3.50.1"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62471055, 62321001, U23B2001, 62101064, 62171057, 62201072"],"award-info":[{"award-number":["62471055, 62321001, U23B2001, 62101064, 62171057, 62201072"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"High-Quality Development Project of the MIIT","award":["2440STCZB2584"],"award-info":[{"award-number":["2440STCZB2584"]}]},{"name":"Ministry of Education and China Mobile Joint Fund","award":["MCM20200202, MCM20180101"],"award-info":[{"award-number":["MCM20200202, MCM20180101"]}]},{"name":"China Postdoctoral Science Foundation","award":["2023TQ0039, 2024M750257, GZC20230320"],"award-info":[{"award-number":["2023TQ0039, 2024M750257, GZC20230320"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2024PTB-004"],"award-info":[{"award-number":["2024PTB-004"]}]},{"name":"2025 Education and Teaching Reform Project Funding at Beijing University of Posts and Telecommunications","award":["2025YZ005"],"award-info":[{"award-number":["2025YZ005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2026,4,2]]},"abstract":"<jats:p>In the era of large language models, Text-to-SQL, as a natural language interface for databases, is playing an increasingly important role. State-of-the-art Text-to-SQL models have achieved impressive accuracy, but their performance critically relies on expert-written evidence. This evidence typically clarifies schema and value linking that existing models struggle to identify. Such limitations stem from the ambiguity of user queries and, more importantly, the complexity of comprehending large-scale and dynamic database values. Consequently, in real-world scenarios where expert assistance is unavailable, existing methods suffer a severe performance collapse, with execution accuracy dropping by over 10%. This underscores their lack of robustness due to the reliance on expert assistance.<\/jats:p>\n                  <jats:p>\n                    To address this, we propose DIVER, a robust system that\n                    <jats:bold>\n                      <jats:italic toggle=\"yes\">automates<\/jats:italic>\n                    <\/jats:bold>\n                    evidence reasoning with dynamic interactive value linking. It leverages a\n                    <jats:italic toggle=\"yes\">compatible toolbox<\/jats:italic>\n                    containing diverse tools to probe the database. Then, restricted by a\n                    <jats:italic toggle=\"yes\">structured workspace (CoTF, Chain of Thoughts and Facts)<\/jats:italic>\n                    , it reflects based on probe results and selects a new tool for next round of probing. Through this automatically iterative process, DIVER identifies schema and value linking missed by existing methods. Based on these accurate linkings, DIVER is able to infer correct usage of SQL functions and formulas and generate high-quality evidence, achieving robust Text-to-SQL without expert assistance. Extensive experiments demonstrate that: 1) The DIVER system significantly enhances the robustness of various Text-to-SQL models, improving performance by up to 10.82% in Execution Accuracy (EX) and 16.09% in Valid Efficiency Score (VES). 2) Our dynamic interactive value linking significantly improves the robustness of existing systems and the accuracy of schema and value linking, especially when confronted with challenges posed by large-scale, dynamic database values.\n                  <\/jats:p>","DOI":"10.1145\/3786640","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T17:54:13Z","timestamp":1775584453000},"page":"1-24","source":"Crossref","is-referenced-by-count":0,"title":["DIVER: A Robust Text-to-SQL System with Dynamic Interactive Value Linking and Evidence Reasoning"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2145-4605","authenticated-orcid":false,"given":"Yafeng","family":"Nan","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3072-7422","authenticated-orcid":false,"given":"Haifeng","family":"Sun","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3345-1732","authenticated-orcid":false,"given":"Zirui","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0829-4624","authenticated-orcid":false,"given":"Qi","family":"Qi","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2352-4731","authenticated-orcid":false,"given":"Guojun","family":"Chu","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1486-0573","authenticated-orcid":false,"given":"Jianxin","family":"Liao","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5113-838X","authenticated-orcid":false,"given":"Dan","family":"Pei","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2182-2228","authenticated-orcid":false,"given":"Jingyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,7]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1448"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1378"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00220"},{"key":"e_1_2_1_4_1","volume-title":"The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Wc5bmZZU9cy","author":"Chang Shuaichen","year":"2023","unstructured":"Shuaichen Chang, Jun Wang, Mingwen Dong, Lin Pan, Henghui Zhu, Alexander Hanbo Li, Wuwei Lan, Sheng Zhang, Jiarong Jiang, Joseph Lilien, Steve Ash, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Patrick Ng, and Bing Xiang. 2023. Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Wc5bmZZU9cy"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","unstructured":"Minghang Deng Ashwin Ramachandran Canwen Xu Lanxiang Hu Zhewei Yao Anupam Datta and Hao Zhang. 2025. ReFoRCE: A Text-to-SQL Agent with Self-Refinement Format Restriction and Column Exploration. arXiv:2502.00675 [cs] doi:10.48550\/arXiv.2502.00675","DOI":"10.48550\/arXiv.2502.00675"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","unstructured":"Xuemei Dong Chao Zhang Yuhang Ge Yuren Mao Yunjun Gao Lu Chen Jinshu Lin and Dongfang Lou. 2023. C3: Zero-Shot Text-to-SQL with ChatGPT. arXiv:2307.07306 [cs] doi:10.48550\/arXiv.2307.07306","DOI":"10.48550\/arXiv.2307.07306"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.702"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.702"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","unstructured":"Dawei Gao Haibin Wang Yaliang Li Xiuyu Sun Yichen Qian Bolin Ding and Jingren Zhou. 2023. Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation. arXiv:2308.15363 [cs] doi:10.48550\/arXiv.2308.15363","DOI":"10.48550\/arXiv.2308.15363"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/3641204.3641221"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","unstructured":"Yingqi Gao Yifu Liu Xiaoxia Li Xiaorong Shi Yin Zhu Yiming Wang Shiqi Li Wei Li Yuntao Hong Zhiling Luo Jinyang Gao Liyu Mou and Yu Li. 2025. A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL. arXiv:2411.08599 [cs] doi:10.48550\/arXiv.2411.08599","DOI":"10.48550\/arXiv.2411.08599"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1444"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-022-00776-8"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2411.07763"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.14778\/3681954.3682003"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","unstructured":"Boyan Li Jiayi Zhang Ju Fan Yanwei Xu Chong Chen Nan Tang and Yuyu Luo. 2025b. Alpha-SQL: Zero-Shot Text-to-SQL Using Monte Carlo Tree Search. arXiv:2502.17248 [cs] doi:10.48550\/arXiv.2502.17248","DOI":"10.48550\/arXiv.2502.17248"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","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. 2025a. OmniSQL: Synthesizing High-Quality Text-to-SQL Data at Scale. arXiv:2503.02240 [cs] doi:10.48550\/arXiv.2503.02240","DOI":"10.48550\/arXiv.2503.02240"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2302.05965"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3654930"},{"key":"e_1_2_1_20_1","first-page":"42330","volume-title":"Proceedings of the 37th International Conference, on Neural Information Processing Systems (NIPS, '23)","author":"Li Jinyang","year":"2024","unstructured":"Jinyang Li, Binyuan Hui, Ge Qu, Jiaxi Yang, Binhua Li, Bowen Li, Bailin Wang, Bowen Qin, Ruiying Geng, Nan Huo, Xuanhe Zhou, Chenhao Ma, Guoliang Li, Kevin C.C. Chang, Fei Huang, Reynold Cheng, and Yongbin Li. 2024a. 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 (NIPS, '23). Curran Associates Inc., Red Hook, NY, USA, 42330-42357."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","unstructured":"Xinyu Liu Shuyu Shen Boyan Li Peixian Ma Runzhi Jiang Yuxin Zhang Ju Fan Guoliang Li Nan Tang and Yuyu Luo. 2025. A Survey of NL2SQL with Large Language Models: Where Are We and Where Are We Going? arXiv:2408.05109 [cs] doi:10.48550\/arXiv.2408.05109","DOI":"10.48550\/arXiv.2408.05109"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3709727"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","unstructured":"Peixian Ma Boyan Li Runzhi Jiang Ju Fan Nan Tang and Yuyu Luo. 2024. A Plug-and-Play Natural Language Rewriter for Natural Language to SQL. arXiv:2412.17068 [cs] doi:10.48550\/arXiv.2412.17068","DOI":"10.48550\/arXiv.2412.17068"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","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. arXiv:2408.07702 [cs] doi:10.48550\/arXiv.2408.07702","DOI":"10.48550\/arXiv.2408.07702"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/EDUCON52537.2022.9766617"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2410.01943"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-1577"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","unstructured":"Mohammadreza Pourreza and Davood Rafiei. 2024. DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models. arXiv:2402.01117 [cs] doi:10.48550\/arXiv.2402.01117","DOI":"10.48550\/arXiv.2402.01117"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2205.06983"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2208.13629"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1910.10683"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","unstructured":"Torsten Scholak Nathan Schucher and Dzmitry Bahdanau. 2021. PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing Marie-Francine Moens Xuanjing Huang Lucia Specia and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics Online and Punta Cana Dominican Republic 9895-9901. doi:10.18653\/v1\/2021.emnlp-main.779","DOI":"10.18653\/v1\/2021.emnlp-main.779"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","unstructured":"Ruoxi Sun Sercan O. Arik Hootan Nakhost Hanjun Dai Rajarishi Sinha Pengcheng Yin and Tomas Pfister. 2023. SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL. arXiv:2306.00739 [cs] doi:10.48550\/arXiv.2306.00739","DOI":"10.48550\/arXiv.2306.00739"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2312.11242"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1911.04942"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2001.11770"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3654992"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","unstructured":"Xiangjin Xie Guangwei Xu Lingyan Zhao and Ruijie Guo. 2025. OpenSearch-SQL: Enhancing Text-to-SQL with Dynamic Few-Shot and Consistency Alignment. arXiv:2502.14913 [cs] doi:10.48550\/arXiv.2502.14913","DOI":"10.48550\/arXiv.2502.14913"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","unstructured":"Wenyi Xu Yuren Mao Xiaolu Zhang Chao Zhang Xuemei Dong Mengfei Zhang Jun Zhou and Yunjun Gao. 2025. DAgent: A Relational Database-Driven Data Analysis Report Generation Agent. arXiv:2503.13269 [cs] doi:10.48550\/arXiv.2503.13269","DOI":"10.48550\/arXiv.2503.13269"},{"key":"e_1_2_1_40_1","first-page":"3","article-title":"Automated Validating and Fixing of Text-to-SQL, Translation with Execution Consistency","volume":"3","author":"Yang Yicun","year":"2025","unstructured":"Yicun Yang, Zhaoguo Wang, Yu Xia, Zhuoran Wei, Haoran Ding, Ruzica Piskac, Haibo Chen, and Jinyang Li. 2025. Automated Validating and Fixing of Text-to-SQL, Translation with Execution Consistency. SIGMOD, Vol. 3, 3 (Feb. 2025).","journal-title":"SIGMOD"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2210.03629"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","unstructured":"Tao Yu Rui Zhang Kai Yang Michihiro Yasunaga Dongxu Wang Zifan Li James Ma Irene Li Qingning Yao Shanelle Roman Zilin Zhang and Dragomir Radev. 2018. Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing Ellen Riloff David Chiang Julia Hockenmaier and Jun'ichi Tsujii (Eds.). Association for Computational Linguistics Brussels Belgium 3911-3921. doi:10.18653\/v1\/D18-1425","DOI":"10.18653\/v1\/D18-1425"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","unstructured":"Hongxin Zhang Weihua Du Jiaming Shan Qinhong Zhou Yilun Du Joshua B. Tenenbaum Tianmin Shu and Chuang Gan. 2024. Building Cooperative Embodied Agents Modularly with Large Language Models. In The Twelfth International Conference on Learning Representations (International Conference on Learning Representations ). arXiv. arXiv:2307.02485 [cs] doi:10.48550\/arXiv.2307.02485","DOI":"10.48550\/arXiv.2307.02485"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00420"}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3786640","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T20:01:01Z","timestamp":1775592061000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3786640"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,2]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,4,2]]}},"alternative-id":["10.1145\/3786640"],"URL":"https:\/\/doi.org\/10.1145\/3786640","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,2]]}}}