@inproceedings{wu-etal-2025-ucs,
    title = "{UCS}-{SQL}: Uniting Content and Structure for Enhanced Semantic Bridging In Text-to-{SQL}",
    author = "Wu, Zhenhe  and
      Li, Zhongqiu  and
      JieZhangChinaTele, JieZhangChinaTele  and
      He, Zhongjiang  and
      Yang, Jian  and
      Zhao, Yu  and
      Fang, Ruiyu  and
      Wang, Bing  and
      Xie, Hongyan  and
      Song, Shuangyong  and
      Li, Zhoujun",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.findings-acl.427/",
    pages = "8156--8168",
    ISBN = "979-8-89176-256-5",
    abstract = "With the rapid advancement of large language models (LLMs), recent researchers have increasingly focused on the superior capabilities of LLMs in text/code understanding and generation to tackle text-to-SQL tasks. Traditional approaches adopt schema linking to first eliminate redundant tables and columns and prompt LLMs for SQL generation. However, they often struggle with accurately identifying corresponding tables and columns, due to discrepancies in naming conventions between natural language questions (NL) and database schemas. Besides, existing methods overlook the challenge of effectively transforming structure information from NL into SQL. To address these limitations, we introduce UCS-SQL, a novel text-to-SQL framework, uniting both content and structure pipes to bridge the gap between NL and SQL. Specifically, the content pipe focuses on identifying key content within the original content, while the structure pipe is dedicated to transforming the linguistic structure from NL to SQL. Additionally, we strategically selects few-shot examples by considering both the SQL Skeleton and Question Expression (SS-QE selection method), thus providing targeted examples for SQL generation. Experimental results on BIRD and Spider demonstrate the effectiveness of our UCS-SQL framework."
}