Cracking SQL Barriers: An LLM-based Dialect Translation System
Summary: CrackSQL leverages LLMs to translate SQL dialects via function-based segmentation, normalization, and translation-irrelevant abstraction for long, multi-syntax queries. A Cross-Dialect Syntax Embedding Model with a Local-to-Global loop enables targeted fixes and iterative translation. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Wei Zhou (Shanghai Jiao Tong University)
- 2. Yuyang Gao (Tsinghua University)
- 3. Xuanhe Zhou (Shanghai Jiao Tong University)
- 4. Guoliang Li (Tsinghua University)
BibTeX Citation
@inproceedings{zhou_sigmod25,
title = {{Cracking SQL Barriers: An LLM-based Dialect Translation System}},
author = {Zhou, Wei and Gao, Yuyang and Zhou, Xuanhe and Li, Guoliang},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725278},
url = {https://dl.acm.org/doi/10.1145/3725278},
year = {2025}
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