Abstract: Text-to-SQL technology enables the automatic conversion of natural language into SQL,significantly lowering the barrier for non-experts to interact with databases.However,in vertical domains such as finance,it still faces two major challenges:complex query intents and ambiguous expressions.To address these,this paper proposes a collaborative framework that integrates Chain-of-Thought reasoning with retrieval augmentation.Firstly,it designs an iterative distillation algorithm that leverages a compact 3-billion-parameter model to automatically generate and verify over 20 000 high-quality Chain-of-Thought seed examples with detailed reasoning steps on the Spider,BIRD,and BookSQL datasets,effectively compensating for the limitations of small models in complex reasoning tasks.Secondly,it introduces an innovative dual-vector retrieval mechanism based on “question ske-letons” and “SQL skeletons” to eliminate interference from table and column names,dynamically incorporating historically similar queries and implicit semantics as examples within prompts,thereby achieving precise alignment of ambiguous domain-specific expressions.Experimental results demonstrate that Qwen2.5-Coder,with only 3 billion parameters,attains 86.5% execution accuracy on Spider-dev—comparable to powerful models such as GPT-4,achieves 59.6% on the more challenging BIRD-dev,outperforming many larger models,and reaches 81.2% on a proprietary financial dataset,exceeding existing methods by over 1 percentage point.This approach delivers high-precision SQL generation for complex and ambiguous natural language queries at low cost and with a relatively small model size.

Key words: Financial information systems, Artificial intelligence, Natural language processing, Text-to-SQL, Large-scale language models