@inproceedings{0d068f33c23b431fa07a0fa11a110ef3,
title = "SQL-QMARS: A Query-Guided Multi-agent Recommendation System for SQL",
abstract = "Large language models (LLMs) advance natural language (NL) interaction with databases by converting queries into SQL. However, users often lack familiarity with database schemas, making it difficult to express precise query requirements. To address this, we propose SQL-QMARS, a multi-agent Text-to-SQL framework designed to interactively clarify user intent. The system evaluates query vagueness using a three-layer metadata structure (theme, table, and field). Based on this evaluation, it dynamically triggers two flows: recommending multi-granular suggestions for vague queries and resolving ambiguities for clear ones. Furthermore, the system supports fusing external data to expand the knowledge source for query suggestions. The demonstration indicates that SQL-QMARS effectively guides users from vague to precise queries, improving the practicality of NL-based database interaction.",
keywords = "interactive Text-to-SQL, multi-agent systems",
author = "Jiawen Xu and Wei Zhou and Yungui Zheng and Huiqi Hu and Peng Cai and Xuan Zhou and Yaoqiang Xu and Chen Qian",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 31st International Conference on Database Systems for Advanced Applications, DASFAA 2026 ; Conference date: 27-04-2026 Through 30-04-2026",
year = "2026",
doi = "10.1007/978-981-92-0378-9\_48",
language = "英语",
isbn = "9789819203772",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "690--694",
editor = "Hyungsoo Jung and Tianzheng Wang and Masashi Toyoda and Hyuk-Yoon Kwon and Jae-woong Lee",
booktitle = "Database Systems for Advanced Applications - 31st International Conference, DASFAA 2026, Proceedings",
address = "德国",
}