CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning

• URL: http://arxiv.org/abs/2505.13271v1
• Date: Mon, 19 May 2025 15:52:19 GMT
• Title: CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning
• Authors: Lei Sheng, Shuai-Shuai Xu,
• Abstract summary: We propose CSC-, a novel method that integrates Self-Consistency and Self-Correction.<n>Our 3B model achieves 65.28% execution accuracy, while the 7B model achieves 69.19%.<n>On the BIRD development set, our 3B model achieves 65.28% execution accuracy, while the 7B model achieves 69.19%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have demonstrated strong capabilities in translating natural language questions about relational databases into SQL queries. In particular, test-time scaling techniques such as Self-Consistency and Self-Correction can enhance SQL generation accuracy by increasing computational effort during inference. However, these methods have notable limitations: Self-Consistency may select suboptimal outputs despite majority votes, while Self-Correction typically addresses only syntactic errors. To leverage the strengths of both approaches, we propose CSC-SQL, a novel method that integrates Self-Consistency and Self-Correction. CSC-SQL selects the two most frequently occurring outputs from parallel sampling and feeds them into a merge revision model for correction. Additionally, we employ the Group Relative Policy Optimization (GRPO) algorithm to fine-tune both the SQL generation and revision models via reinforcement learning, significantly enhancing output quality. Experimental results confirm the effectiveness and generalizability of CSC-SQL. On the BIRD development set, our 3B model achieves 65.28% execution accuracy, while the 7B model achieves 69.19%. The code will be open sourced at https://github.com/CycloneBoy/csc_sql.

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