@inproceedings{zhu-etal-2025-text2sql,
title = "{T}ext2{S}ql: Pure Fine-Tuning and Pure Knowledge Distillation",
author = "Zhu, Gao yu and
Shao, Wei and
Zhu, Xichou and
Yu, Lei and
Guo, Jiafeng and
Cheng, Xueqi",
editor = "Chen, Weizhu and
Yang, Yi and
Kachuee, Mohammad and
Fu, Xue-Yong",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-industry.5/",
doi = "10.18653/v1/2025.naacl-industry.5",
pages = "54--61",
ISBN = "979-8-89176-194-0",
abstract = "Text2Sql is a task that converts natural language questions into SQL queries. In previous research on LLM fine-tuning, researchers typically input both the entire database schema and the natural language question into the model. This approach has two issues: 1) the model{'}s context is limited when dealing with a large number of database tables; 2) the question is often related to only a few tables, leading to excessive irrelevant information that distracts the model. To address these issues, we employed pure fine-tuning strategy to reduce redundancy. The model fine-tuned with pure prompts, using prompts that are only 53{\%} of the baseline length, outperforms the baseline (fine-tuned with all tables in the prompt) by 8.2{\%} and 8.6{\%} in Test-suite accuracy (TS) and exact-set-match accuracy (EM), respectively, on the Spider dev set. Under the most refined Spider dev set of prompts, the model achieves TS and EM scores of 73.5{\%} and 75.4{\%}, respectively, approaching state-of-the-art (SOTA) levels. To leverage the capabilities of the model with pure prompts, we applied pure knowledge distillation strategy to transfer its abilities. The distilled student model achieved a 1.9{\%} improvement in TS, while the teacher model{'}s prompt length was only 23{\%} of that of the student model."
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<abstract>Text2Sql is a task that converts natural language questions into SQL queries. In previous research on LLM fine-tuning, researchers typically input both the entire database schema and the natural language question into the model. This approach has two issues: 1) the model’s context is limited when dealing with a large number of database tables; 2) the question is often related to only a few tables, leading to excessive irrelevant information that distracts the model. To address these issues, we employed pure fine-tuning strategy to reduce redundancy. The model fine-tuned with pure prompts, using prompts that are only 53% of the baseline length, outperforms the baseline (fine-tuned with all tables in the prompt) by 8.2% and 8.6% in Test-suite accuracy (TS) and exact-set-match accuracy (EM), respectively, on the Spider dev set. Under the most refined Spider dev set of prompts, the model achieves TS and EM scores of 73.5% and 75.4%, respectively, approaching state-of-the-art (SOTA) levels. To leverage the capabilities of the model with pure prompts, we applied pure knowledge distillation strategy to transfer its abilities. The distilled student model achieved a 1.9% improvement in TS, while the teacher model’s prompt length was only 23% of that of the student model.</abstract>
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%0 Conference Proceedings
%T Text2Sql: Pure Fine-Tuning and Pure Knowledge Distillation
%A Zhu, Gao yu
%A Shao, Wei
%A Zhu, Xichou
%A Yu, Lei
%A Guo, Jiafeng
%A Cheng, Xueqi
%Y Chen, Weizhu
%Y Yang, Yi
%Y Kachuee, Mohammad
%Y Fu, Xue-Yong
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-194-0
%F zhu-etal-2025-text2sql
%X Text2Sql is a task that converts natural language questions into SQL queries. In previous research on LLM fine-tuning, researchers typically input both the entire database schema and the natural language question into the model. This approach has two issues: 1) the model’s context is limited when dealing with a large number of database tables; 2) the question is often related to only a few tables, leading to excessive irrelevant information that distracts the model. To address these issues, we employed pure fine-tuning strategy to reduce redundancy. The model fine-tuned with pure prompts, using prompts that are only 53% of the baseline length, outperforms the baseline (fine-tuned with all tables in the prompt) by 8.2% and 8.6% in Test-suite accuracy (TS) and exact-set-match accuracy (EM), respectively, on the Spider dev set. Under the most refined Spider dev set of prompts, the model achieves TS and EM scores of 73.5% and 75.4%, respectively, approaching state-of-the-art (SOTA) levels. To leverage the capabilities of the model with pure prompts, we applied pure knowledge distillation strategy to transfer its abilities. The distilled student model achieved a 1.9% improvement in TS, while the teacher model’s prompt length was only 23% of that of the student model.
%R 10.18653/v1/2025.naacl-industry.5
%U https://aclanthology.org/2025.naacl-industry.5/
%U https://doi.org/10.18653/v1/2025.naacl-industry.5
%P 54-61
Markdown (Informal)
[Text2Sql: Pure Fine-Tuning and Pure Knowledge Distillation](https://aclanthology.org/2025.naacl-industry.5/) (Zhu et al., NAACL 2025)
ACL
- Gao yu Zhu, Wei Shao, Xichou Zhu, Lei Yu, Jiafeng Guo, and Xueqi Cheng. 2025. Text2Sql: Pure Fine-Tuning and Pure Knowledge Distillation. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track), pages 54–61, Albuquerque, New Mexico. Association for Computational Linguistics.