Enhanced Financial Text-to-SQL Generation via Fine-Grained SQL Refinement

Text-to-SQL systems aim to enable users to query structured data using natural language, and recent advances in large language models have significantly improved their performance on general-domain benchmarks. However, existing approaches remain largely domain-agnostic and struggle to adapt to financial applications, where the diversity of SQL dialects across database management systems and the complexity of domain-specific semantics frequently lead to substantial degradation in both syntactic correctness and logical consistency. To address these limitations, we propose a financial-domain-oriented Text-to-SQL framework that jointly enhances SQL generation from two complementary perspectives. First, we introduce a syntax-aware mechanism that mitigates SQL dialect confusion and improves the robustness of code generation across heterogeneous financial databases. Second, we incorporate a semantic alignment module that captures implicit financial business rules and domain terminology, enabling the model to produce SQL that better reflects users’ intent. Experimental results on a real-world financial dataset demonstrate that our method substantially reduces both syntax and logic errors, highlighting the necessity of domain-specific adaptation for reliable Text-to-SQL deployment in specialized environments.