Feather-SQL: A Lightweight NL2SQL Framework with Dual-Model
  Collaboration Paradigm for Small Language Models
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.
67e27583cf3845b431740913
Wenqi Pei
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2025-03-25T14:36:24.705Z
65e7d63b14856e8859f1924c
/avatars/bc58ab252c7b4d95ff99e4506fb8d3e9.svg
Pei Wenqi
user
CedPei
67e27583cf3845b431740914
Hailing Xu
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2025-03-28T08:38:20.843Z
66310df038af94f590d9e59c
/avatars/7d737a8366a6cab6c9bd32cfcd647138.svg
hailingxu
user
racoon23
67e27583cf3845b431740915
Hengyuan Zhao
67e27583cf3845b431740916
Shizheng Hou
claimed_verified
2025-07-02T16:08:16.573Z
651fc45f736eb79aa0dcd0ab
/avatars/ae5aa5a4a7917f7c52cc6249ea12e01d.svg
Shizheng Hou
user
Shizheng
67e27583cf3845b431740917
Han Chen
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2025-03-25T14:36:43.002Z
6399c67bf78f75ae73146760
/avatars/d1c3b0eff67b598a1ad58e297382957f.svg
CHEN Han
user
Concyclics
67e27583cf3845b431740918
Zining Zhang
67e27583cf3845b431740919
Pingyi Luo
67e27583cf3845b43174091a
Bingsheng He
Feather-SQL: A Lightweight NL2SQL Framework with Dual-Model
  Collaboration Paradigm for Small Language Models
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.
Feather-SQL enhances NL2SQL performance for small language models using schema pruning, multi-path generation, and collaboration with a fine-tuned SQL specialist.
schema pruning multi-path generation multi-candidate generation 1+1 Model Collaboration Paradigm NL2SQL small language models SQL specialist strong general-purpose chat model