@inproceedings{chen-etal-2025-adept,
title = "{ADEPT}-{SQL}: A High-performance Text-to-{SQL} Application for Real-World Enterprise-Level Databases",
author = "Chen, Yongnan and
Chang, Zhuo and
Gu, Shijia and
Zong, Yuanhang and
Zhang, Mei and
Wang, Shiyu and
He, Zixiang and
Chen, HongZhi and
Jin, Wei and
Cui, Bin",
editor = "Mishra, Pushkar and
Muresan, Smaranda and
Yu, Tao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-demo.27/",
doi = "10.18653/v1/2025.acl-demo.27",
pages = "275--283",
ISBN = "979-8-89176-253-4",
abstract = "This paper presents Adept-SQL, a domain-adapted Text2SQL system that addresses critical deployment challenges in professional fields. While modern LLM-based solutions excel on academic benchmarks, we identify three persistent limitations in industrial application: domain-specific knowledge barriers, the schemas complexity in the real world, and the prohibitive computational costs of large LLMs. Our framework introduces two key innovations: a three-stage grounding mechanism combining dynamic terminology expansion, focused schema alignment, and historical query retrieval; coupled with a hybrid prompting architecture that decomposes SQL generation into schema-aware hinting, term disambiguation, and few-shot example incorporation phases. This approach enables efficient execution using smaller open-source LLMs while maintaining semantic precision. Deployed in petroleum engineering domains, our system achieves 97{\%} execution accuracy on real-world databases, demonstrating 49{\%} absolute improvement over SOTA baselines. We release implementation code to advance research in professional Text2SQL systems."
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%0 Conference Proceedings
%T ADEPT-SQL: A High-performance Text-to-SQL Application for Real-World Enterprise-Level Databases
%A Chen, Yongnan
%A Chang, Zhuo
%A Gu, Shijia
%A Zong, Yuanhang
%A Zhang, Mei
%A Wang, Shiyu
%A He, Zixiang
%A Chen, HongZhi
%A Jin, Wei
%A Cui, Bin
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F chen-etal-2025-adept
%X This paper presents Adept-SQL, a domain-adapted Text2SQL system that addresses critical deployment challenges in professional fields. While modern LLM-based solutions excel on academic benchmarks, we identify three persistent limitations in industrial application: domain-specific knowledge barriers, the schemas complexity in the real world, and the prohibitive computational costs of large LLMs. Our framework introduces two key innovations: a three-stage grounding mechanism combining dynamic terminology expansion, focused schema alignment, and historical query retrieval; coupled with a hybrid prompting architecture that decomposes SQL generation into schema-aware hinting, term disambiguation, and few-shot example incorporation phases. This approach enables efficient execution using smaller open-source LLMs while maintaining semantic precision. Deployed in petroleum engineering domains, our system achieves 97% execution accuracy on real-world databases, demonstrating 49% absolute improvement over SOTA baselines. We release implementation code to advance research in professional Text2SQL systems.
%R 10.18653/v1/2025.acl-demo.27
%U https://aclanthology.org/2025.acl-demo.27/
%U https://doi.org/10.18653/v1/2025.acl-demo.27
%P 275-283
Markdown (Informal)
[ADEPT-SQL: A High-performance Text-to-SQL Application for Real-World Enterprise-Level Databases](https://aclanthology.org/2025.acl-demo.27/) (Chen et al., ACL 2025)
ACL
- Yongnan Chen, Zhuo Chang, Shijia Gu, Yuanhang Zong, Mei Zhang, Shiyu Wang, Zixiang He, HongZhi Chen, Wei Jin, and Bin Cui. 2025. ADEPT-SQL: A High-performance Text-to-SQL Application for Real-World Enterprise-Level Databases. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 275–283, Vienna, Austria. Association for Computational Linguistics.