RingSQL: Generating Synthetic Data with Schema-Independent Templates for Text-to-SQL Reasoning Models

2026-01-09

arXiv.org

AI Summary

This work addresses the scarcity of high-quality training data for text-to-SQL models, a key limitation exacerbated by existing synthetic data generation methods that struggle to balance scalability with SQL correctness. To overcome this, the authors propose a hybrid data generation framework that introduces database-schema-agnostic SQL templates for the first time, eliminating the need for manual template design per database. Coupled with controlled natural language paraphrasing powered by large language models (LLMs), the approach substantially enhances linguistic diversity while preserving SQL accuracy. Evaluated across six mainstream text-to-SQL benchmarks, the method achieves an average accuracy improvement of 2.3% over current synthetic data techniques, demonstrating its effectiveness and generalizability.

Technology Category

Natural Language Processing: Code Generation / Program Synthesis from Natural LanguageMachine Learning: Large Multimodal Models (LMMs)Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsSearch and Retrieval-Augmented AI: Large language models for searchEconomics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Recent advances in text-to-SQL systems have been driven by larger models and improved datasets, yet progress is still limited by the scarcity of high-quality training data. Manual data creation is expensive, and existing synthetic methods trade off reliability and scalability. Template-based approaches ensure correct SQL but require schema-specific templates, while LLM-based generation scales easily but lacks quality and correctness guarantees. We introduce RingSQL, a hybrid data generation framework that combines schema-independent query templates with LLM-based paraphrasing of natural language questions. This approach preserves SQL correctness across diverse schemas while providing broad linguistic variety. In our experiments, we find that models trained using data produced by RingSQL achieve an average gain in accuracy of +2.3% across six text-to-SQL benchmarks when compared to models trained on other synthetic data. We make our code available at https://github.com/nu-c3lab/RingSQL.

Problem

• text-to-SQL
• synthetic data
• data scarcity
• schema independence
• SQL correctness

Innovation

• schema-independent templates
• synthetic data generation
• text-to-SQL
• LLM-based paraphrasing
• SQL correctness