Feather-SQL: A Lightweight NL2SQL Framework with Dual-Model
  Collaboration Paradigm for Small Language Models
Authors: name: Wenqi Pei, name: Hailing Xu, name: Hengyuan Zhao, name: Shizheng Hou, name: Han Chen, name: Zining Zhang, name: Pingyi Luo, name: Bingsheng He

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.