Hybrid Template-Language Model Boosts Synthetic Data for Text-to-SQL Models

Global: Hybrid Template-Language Model Boosts Synthetic Data for Text-to-SQL Models

On January 9, 2026, a group of researchers including Marko Sterbentz, Kevin Cushing, Cameron Barrie, and Kristian J. Hammond released a paper on arXiv that proposes a hybrid framework for generating synthetic training data for text-to-SQL reasoning models. The study, identified as arXiv:2601.05451, addresses the persistent shortage of high‑quality annotated examples that hampers progress in natural language interfaces to databases.

Background on Text-to‑SQL Training

Text‑to‑SQL systems translate natural‑language questions into executable SQL queries. Existing datasets are limited in size, and manual annotation is costly. Prior synthetic approaches either rely on schema‑specific templates, guaranteeing SQL correctness but lacking linguistic diversity, or on large language models (LLMs) that generate varied phrasing but can produce incorrect queries.

RingSQL Framework Overview

The authors introduce RingSQL, a two‑stage pipeline that first applies schema‑independent query templates to produce correct SQL statements across arbitrary database schemas. In the second stage, an LLM paraphrases the associated natural‑language questions, preserving the original intent while expanding linguistic variety. This combination seeks to retain the reliability of template methods while leveraging the scalability of LLM‑driven generation.

Experimental Results

Evaluation across six established text‑to‑SQL benchmarks shows that models trained on RingSQL‑generated data achieve an average accuracy improvement of +2.3 percentage points compared with models trained on other synthetic datasets. The authors attribute the gain to the balanced mix of syntactic correctness and diverse phrasing.

Availability and Future Directions

The codebase for RingSQL has been released publicly, and the authors indicate plans to extend the framework to support additional database schemas and to explore automated quality‑assessment metrics for generated questions. The paper is classified under Machine Learning (cs.LG) and Computation and Language (cs.CL) and carries the DOI https://doi.org/10.48550/arXiv.2601.05451.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.