@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://preview.aclanthology.org/ingest-ijcnlp-aacl/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."
}