LIST: Linearly Incremental SQL Translator for Single-Hop Reasoning, Generation and Verification
Kaiyuan Guan, Ruoxin Li, Xudong Guo, Zhenning Huang, Xudong Weng, Hehuan Liu, Zheng Wei, Zang Li
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.
BibTeX
@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://aclanthology.org/2025.findings-acl.1277/",
doi = "10.18653/v1/2025.findings-acl.1277",
pages = "24886--24897",
ISBN = "979-8-89176-256-5"
}