@inproceedings{duan-etal-2025-pdc,
title = "{PDC} {\&} {DM}-{SFT}: A Road for {LLM} {SQL} Bug-Fix Enhancing",
author = "Duan, Yiwen and
Yu, Yonghong and
Zhao, Xiaoming and
Wu, Yichang and
Liu, Wenbo",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Darwish, Kareem and
Agarwal, Apoorv",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-industry.7/",
pages = "76--90",
abstract = "Code Large Language Models (Code LLMs), such as Code llama and DeepSeek-Coder, have demonstrated exceptional performance in the code generation tasks. However, most existing models focus on the abilities of generating correct code, but often struggle with bug repair. We introduce a suit of methods to enhance LLM`s SQL bug-fixing abilities. The methods are mainly consisted of two parts: A Progressive Dataset Construction (PDC) from scratch and Dynamic Mask Supervised Fine-tuning (DM-SFT). PDC proposes two data expansion methods from the perspectives of breadth first and depth first respectively. DM-SFT introduces an efficient bug-fixing supervised learning approach, which effectively reduce the total training steps and mitigate the {\textquotedblleft}disorientation{\textquotedblright} in SQL code bug-fixing training. In our evaluation, the code LLM models trained with two methods have exceeds all current best performing model which size is much larger."
}
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%0 Conference Proceedings
%T PDC & DM-SFT: A Road for LLM SQL Bug-Fix Enhancing
%A Duan, Yiwen
%A Yu, Yonghong
%A Zhao, Xiaoming
%A Wu, Yichang
%A Liu, Wenbo
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%Y Darwish, Kareem
%Y Agarwal, Apoorv
%S Proceedings of the 31st International Conference on Computational Linguistics: Industry Track
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F duan-etal-2025-pdc
%X Code Large Language Models (Code LLMs), such as Code llama and DeepSeek-Coder, have demonstrated exceptional performance in the code generation tasks. However, most existing models focus on the abilities of generating correct code, but often struggle with bug repair. We introduce a suit of methods to enhance LLM‘s SQL bug-fixing abilities. The methods are mainly consisted of two parts: A Progressive Dataset Construction (PDC) from scratch and Dynamic Mask Supervised Fine-tuning (DM-SFT). PDC proposes two data expansion methods from the perspectives of breadth first and depth first respectively. DM-SFT introduces an efficient bug-fixing supervised learning approach, which effectively reduce the total training steps and mitigate the “disorientation” in SQL code bug-fixing training. In our evaluation, the code LLM models trained with two methods have exceeds all current best performing model which size is much larger.
%U https://aclanthology.org/2025.coling-industry.7/
%P 76-90
Markdown (Informal)
[PDC & DM-SFT: A Road for LLM SQL Bug-Fix Enhancing](https://aclanthology.org/2025.coling-industry.7/) (Duan et al., COLING 2025)
ACL
- Yiwen Duan, Yonghong Yu, Xiaoming Zhao, Yichang Wu, and Wenbo Liu. 2025. PDC & DM-SFT: A Road for LLM SQL Bug-Fix Enhancing. In Proceedings of the 31st International Conference on Computational Linguistics: Industry Track, pages 76–90, Abu Dhabi, UAE. Association for Computational Linguistics.