CodeS: Towards Building Open-source Language Models for Text-to-SQL

TL;DR

CodeS introduces an open-source series of language models tailored for text-to-SQL translation, achieving state-of-the-art accuracy and robustness while addressing privacy and accessibility concerns associated with proprietary models.

Contribution

The paper presents CodeS, a fully open-source, pre-trained language model series specifically designed for text-to-SQL tasks, with innovative training and augmentation techniques.

Findings

CodeS achieves new SOTA accuracy on multiple benchmarks.

CodeS demonstrates high robustness across diverse datasets.

Open-source models outperform comparable closed-source models.

Abstract

Language models have shown promising performance on the task of translating natural language questions into SQL queries (Text-to-SQL). However, most of the state-of-the-art (SOTA) approaches rely on powerful yet closed-source large language models (LLMs), such as ChatGPT and GPT-4, which may have the limitations of unclear model architectures, data privacy risks, and expensive inference overheads. To address the limitations, we introduce CodeS, a series of pre-trained language models with parameters ranging from 1B to 15B, specifically designed for the text-to-SQL task. CodeS is a fully open-source language model, which achieves superior accuracy with much smaller parameter sizes. This paper studies the research challenges in building CodeS. To enhance the SQL generation abilities of CodeS, we adopt an incremental pre-training approach using a specifically curated SQL-centric corpus. Based on this, we address the challenges of schema linking and rapid domain adaptation through strategic prompt construction and a bi-directional data augmentation technique. We conduct comprehensive evaluations on multiple datasets, including the widely used Spider benchmark, the newly released BIRD benchmark, robustness-diagnostic benchmarks such as Spider-DK, Spider-Syn, Spider-Realistic, and Dr.Spider, as well as two real-world datasets created for financial and academic applications. The experimental results show that our CodeS achieves new SOTA accuracy and robustness on nearly all challenging text-to-SQL benchmarks.