Integrating Reasoning and Generalization in Text-to-SQL via Self-Enhanced Fine-Tuning

• URL: http://arxiv.org/abs/2606.15598v1
• Date: Sun, 14 Jun 2026 04:52:58 GMT
• Title: Integrating Reasoning and Generalization in Text-to-SQL via Self-Enhanced Fine-Tuning
• Abstract summary: CoTE-enhanced to enhance the LLM-based text-to-generation with three key innovations.<n>Self-enhancement, structured reasoning, and execution-time feedback within an LLM-based framework for text-to-generation design.
• Score: 11.942932343145356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-SQL aims to translate natural language questions into executable SQL queries over structured databases, enabling non-expert users to access data intuitively. While recent advances in large language models (LLMs) have shown promise in this task, existing LLM-based approaches often struggle to strike a balance between strong reasoning capabilities and robust generalization. To address these limitations, we propose CoTE-SQL to enhance the LLM-based text-to-SQL generation with three key innovations: (i) self-enhanced reasoning traces distilled from LLMs without human annotation, (ii) structured chain-of-thought (CoT) prompting with modular decomposition and examples retrieval, and (iii) error-aware revision based on SQL execution feedback. Extensive experiments on the Spider and Bird benchmarks demonstrate that CoTE-SQL achieves new state-of-the-art performance among methods built on open-source LLMs with comparable model sizes on Bird (53.39% EX / 59.02 VES) and strong results on Spider (79.60% EX / 77.19 VES), with especially significant gains on complex queries. Results highlight the effectiveness of combining self-enhancement, structured reasoning, and execution-time feedback within an LLM-based framework for text-to-SQL design.

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