@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",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T LIST: Linearly Incremental SQL Translator for Single-Hop Reasoning, Generation and Verification
%A Guan, Kaiyuan
%A Li, Ruoxin
%A Guo, Xudong
%A Huang, Zhenning
%A Weng, Xudong
%A Liu, Hehuan
%A Wei, Zheng
%A Li, Zang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F guan-etal-2025-list
%X 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.
%R 10.18653/v1/2025.findings-acl.1277
%U https://aclanthology.org/2025.findings-acl.1277/
%U https://doi.org/10.18653/v1/2025.findings-acl.1277
%P 24886-24897
Markdown (Informal)
[LIST: Linearly Incremental SQL Translator for Single-Hop Reasoning, Generation and Verification](https://aclanthology.org/2025.findings-acl.1277/) (Guan et al., Findings 2025)
ACL
- Kaiyuan Guan, Ruoxin Li, Xudong Guo, Zhenning Huang, Xudong Weng, Hehuan Liu, Zheng Wei, and Zang Li. 2025. LIST: Linearly Incremental SQL Translator for Single-Hop Reasoning, Generation and Verification. In Findings of the Association for Computational Linguistics: ACL 2025, pages 24886–24897, Vienna, Austria. Association for Computational Linguistics.