@inproceedings{pei-etal-2025-feather,
title = "Feather-{SQL}: A Lightweight {NL}2{SQL} Framework with Dual-Model Collaboration Paradigm for Small Language Models",
author = "Pei, Wenqi and
Xu, Hailing and
Zhao, Henry Hengyuan and
Hou, Shizheng and
Han, Chen and
Zhang, Zining and
Pingyi, Luo and
He, Bingsheng",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-ijcnlp.130/",
pages = "2069--2086",
ISBN = "979-8-89176-303-6",
abstract = "Natural Language to SQL (NL2SQL) has seen significant advancements with large language models (LLMs). However, these models often depend on closed-source methods and high computational resources, posing challenges in data privacy and deployment. In contrast, small language models (SLMs) struggle with NL2SQL tasks, exhibiting poor performance and incompatibility with existing frameworks. To address these issues, we introduce $\textbf{Feather-SQL}$, a new lightweight framework tailored for SLMs. Feather-SQL improves SQL executability and accuracy through: (i) schema pruning and linking, (ii) multi-path and multi-candidate generation. Additionally, we introduce $\textbf{1+1 Model Collaboration Paradigm}$, which pairs a strong general-purpose chat model with a fine-tuned SQL model, combining strong analytical reasoning with high-precision SQL generation. Experimental results on BIRD demonstrate that Feather-SQL improves NL2SQL performance on SLMs, with around 10{\%} boost for models without fine-tuning. The proposed paradigm raises the accuracy ceiling of SLMs to 54.76{\%}, highlighting its effectiveness."
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<abstract>Natural Language to SQL (NL2SQL) has seen significant advancements with large language models (LLMs). However, these models often depend on closed-source methods and high computational resources, posing challenges in data privacy and deployment. In contrast, small language models (SLMs) struggle with NL2SQL tasks, exhibiting poor performance and incompatibility with existing frameworks. To address these issues, we introduce Feather-SQL, a new lightweight framework tailored for SLMs. Feather-SQL improves SQL executability and accuracy through: (i) schema pruning and linking, (ii) multi-path and multi-candidate generation. Additionally, we introduce 1+1 Model Collaboration Paradigm, which pairs a strong general-purpose chat model with a fine-tuned SQL model, combining strong analytical reasoning with high-precision SQL generation. Experimental results on BIRD demonstrate that Feather-SQL improves NL2SQL performance on SLMs, with around 10% boost for models without fine-tuning. The proposed paradigm raises the accuracy ceiling of SLMs to 54.76%, highlighting its effectiveness.</abstract>
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%0 Conference Proceedings
%T Feather-SQL: A Lightweight NL2SQL Framework with Dual-Model Collaboration Paradigm for Small Language Models
%A Pei, Wenqi
%A Xu, Hailing
%A Zhao, Henry Hengyuan
%A Hou, Shizheng
%A Han, Chen
%A Zhang, Zining
%A Pingyi, Luo
%A He, Bingsheng
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-303-6
%F pei-etal-2025-feather
%X Natural Language to SQL (NL2SQL) has seen significant advancements with large language models (LLMs). However, these models often depend on closed-source methods and high computational resources, posing challenges in data privacy and deployment. In contrast, small language models (SLMs) struggle with NL2SQL tasks, exhibiting poor performance and incompatibility with existing frameworks. To address these issues, we introduce Feather-SQL, a new lightweight framework tailored for SLMs. Feather-SQL improves SQL executability and accuracy through: (i) schema pruning and linking, (ii) multi-path and multi-candidate generation. Additionally, we introduce 1+1 Model Collaboration Paradigm, which pairs a strong general-purpose chat model with a fine-tuned SQL model, combining strong analytical reasoning with high-precision SQL generation. Experimental results on BIRD demonstrate that Feather-SQL improves NL2SQL performance on SLMs, with around 10% boost for models without fine-tuning. The proposed paradigm raises the accuracy ceiling of SLMs to 54.76%, highlighting its effectiveness.
%U https://aclanthology.org/2025.findings-ijcnlp.130/
%P 2069-2086
Markdown (Informal)
[Feather-SQL: A Lightweight NL2SQL Framework with Dual-Model Collaboration Paradigm for Small Language Models](https://aclanthology.org/2025.findings-ijcnlp.130/) (Pei et al., Findings 2025)
ACL
- Wenqi Pei, Hailing Xu, Henry Hengyuan Zhao, Shizheng Hou, Chen Han, Zining Zhang, Luo Pingyi, and Bingsheng He. 2025. Feather-SQL: A Lightweight NL2SQL Framework with Dual-Model Collaboration Paradigm for Small Language Models. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 2069–2086, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.