@inproceedings{guan-etal-2025-list,
    title = "{LIST}: Linearly Incremental {SQL} Translator for Single-Hop Reasoning, Generation and Verification",
    author = "Guan, Kaiyuan  and
      Li, Ruoxin  and
      Guo, Xudong  and
      Huang, Zhenning  and
      Weng, Xudong  and
      Liu, Hehuan  and
      Wei, Zheng  and
      Li, Zang",
    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.1277/",
    pages = "24886--24897",
    ISBN = "979-8-89176-256-5",
    abstract = "SQL languages often feature nested structures that require robust interaction with databases. Aside from the well-validated schema linking methods on PLMs and LLMs, we introduce the Linearly Incremental SQL Translator (LIST), a novel algorithmic toolkit designed to leverage the notable reasoning and tool interaction capabilities inherent in LLMs. LIST transforms complex SQL queries into grammatically verifiable sub-queries which are arranged sequentially to reflect single-hop reasoning steps, enhancing both the granularity and accuracy of database interactions. With in-context learning, our experiments demonstrated significant improvements, achieving notable performance of 60.56{\%} and 56.32{\%} on the BIRD dataset with GPT-4o and Llama-3-70B-Instruct. To the best of our knowledge, this achieves SOTA performance among non-schema linking methods, also surpassing a series of schema linking based approaches at a comparable or better cost."
}