# DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models

**Authors:** Mohammadreza Pourreza, Davood Rafiei  
**arXiv:** [2402.01117](https://arxiv.org/abs/2402.01117)  
**Date:** 2024-02-02  
**Summary page:** https://www.summarizepaper.com/fr/paper/2402.01117v1/

## Abstract

Leading models for the text-to-SQL task heavily rely on proprietary Large Language Models (LLMs), posing concerns over data privacy. Closing the performance gap between small open-source models and large proprietary models is crucial to mitigate this reliance. To this end, we introduce a novel two-stage fine-tuning approach that decomposes the task into two simpler tasks. Through comprehensive evaluation on two large cross-domain datasets and two small LLMs, we show that this approach improves execution accuracy by 3 to 7 percent, effectively aligning the performance of open-source models with their proprietary counterparts.