SPOC-SQL: Stage-wise Preference Optimization for Controllable Text-to-SQL

2026-08-23

AI Summary

为解决Text-to-SQL任务中缺乏阶段反馈和控制的问题，提出SPOC-SQL方法，通过分解任务并优化关键决策点来增强结构化决策。

Abstract

Text-to-SQL aims to translate natural language questions into executable SQL queries over relational databases, requiring multi-stage structured reasoning over database schemas and query constraints. However, existing methods treat this task as single-step generation, where models optimize entire SQL sequences without targeted feedback at key decision points and lack support for interacting with and controlling the intermediate generation process. To address this issue, we propose SPOC-SQL, which decomposes Text-to-SQL into four sequential subtasks following standard SQL execution logic and designs stage-specific optimization strategies for the model to learn key decisions. Specifically, we propose the implementation of fine-grained preference optimisation at key decision points across SQL stages, with the objective of enhancing structured decision-making during query construction. Furthermore, a structured decomposition strategy is designed, facilitating stage-wise intervention and correction through explicit intermediate representations. This results in more controllable and reliable SQL generation. Experiments demonstrate that incorporating stage-wise human knowledge consistently improves performance, validating the effectiveness of stage perception controllable generation.

Problem

• Text-to-SQL
• structured reasoning
• key decision points
• controllable generation
• intermediate generation process

Innovation

• Stage-wise Preference Optimization
• Text-to-SQL
• Structured Reasoning
• Controllable Generation
• Intermediate Representations