title: Feather-SQL: A Lightweight NL2SQL Framework with Dual-Model
    Collaboration Paradigm for Small Language Models
summary: Natural Language to SQL (NL2SQL) has seen significant advancements with large
  language models (LLMs). However, these models often depend on closed-source
  systems 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 1) schema pruning and linking, 2) multi-path and
  multi-candidate generation. Additionally, we introduce the 1+1 Model
  Collaboration Paradigm, which pairs a strong general-purpose chat model with a
  fine-tuned SQL specialist, 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.
paper_authors._id: 67e27583cf3845b431740913
paper_authors.name: Wenqi Pei
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T14:36:24.705Z
paper_authors._id: 65e7d63b14856e8859f1924c
paper_authors.avatarUrl: /avatars/bc58ab252c7b4d95ff99e4506fb8d3e9.svg
paper_authors.fullname: Pei Wenqi
paper_authors.type: user
paper_authors.user: CedPei
paper_authors._id: 67e27583cf3845b431740914
paper_authors.name: Hailing Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-28T08:38:20.843Z
paper_authors._id: 66310df038af94f590d9e59c
paper_authors.avatarUrl: /avatars/7d737a8366a6cab6c9bd32cfcd647138.svg
paper_authors.fullname: hailingxu
paper_authors.type: user
paper_authors.user: racoon23
paper_authors._id: 67e27583cf3845b431740915
paper_authors.name: Hengyuan Zhao
paper_authors._id: 67e27583cf3845b431740916
paper_authors.name: Shizheng Hou
paper_authors._id: 67e27583cf3845b431740917
paper_authors.name: Han Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T14:36:43.002Z
paper_authors._id: 6399c67bf78f75ae73146760
paper_authors.avatarUrl: /avatars/d1c3b0eff67b598a1ad58e297382957f.svg
paper_authors.fullname: CHEN Han
paper_authors.type: user
paper_authors.user: Concyclics
paper_authors._id: 67e27583cf3845b431740918
paper_authors.name: Zining Zhang
paper_authors._id: 67e27583cf3845b431740919
paper_authors.name: Pingyi Luo
paper_authors._id: 67e27583cf3845b43174091a
paper_authors.name: Bingsheng He
paper_title: Feather-SQL: A Lightweight NL2SQL Framework with Dual-Model
    Collaboration Paradigm for Small Language Models
paper_summary: Natural Language to SQL (NL2SQL) has seen significant advancements with large
  language models (LLMs). However, these models often depend on closed-source
  systems 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 1) schema pruning and linking, 2) multi-path and
  multi-candidate generation. Additionally, we introduce the 1+1 Model
  Collaboration Paradigm, which pairs a strong general-purpose chat model with a
  fine-tuned SQL specialist, 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.
paper_ai_summary: Feather-SQL enhances NL2SQL performance for small language models using schema pruning, multi-path generation, and collaboration with a fine-tuned SQL specialist.
paper_ai_keywords: schema pruning multi-path generation multi-candidate generation 1+1 Model Collaboration Paradigm NL2SQL small language models SQL specialist strong general-purpose chat model