CLFeb 15, 2025

BASE-SQL: A powerful open source Text-To-SQL baseline approach

arXiv:2502.10739v110 citationsh-index: 3Has Code
Originality Incremental advance
AI Analysis

It provides an easy-to-implement, cost-effective baseline for Text-to-SQL, addressing data privacy and efficiency concerns in real-world applications, though it is incremental in nature.

The paper tackles the problem of generating SQL queries from natural language by proposing BASE-SQL, a pipeline-based method using open-source model fine-tuning, which achieves 67.47% accuracy on BIRD and 88.9% on Spider, outperforming other open-source methods and some closed-source ones.

The conversion of natural language into SQL language for querying databases (Text-to-SQL) has broad application prospects and has attracted widespread attention. At present, the mainstream Text-to-SQL methods are mainly divided into in-context learning (ICL) based methods and supervised fine-tuning (SFT) based methods. ICL-based methods can achieve relatively good results thanks to the use of the most advanced closed-source models. However, in real-world application scenarios, factors such as data privacy, SQL generation efficiency and cost need to be considered. SFT-based methods have certain advantages. At present, methods based on fine-tuning of open source models lack easy-to-implement and effective (cost-effective) baseline methods. We propose a pipeline-based method using open source model fine-tuning, referred to as BASE-SQL, which includes four components: Schema Linking, Candidate SQL Generate, SQL Revision and SQL Merge Revision. Experimental results show that BASE-SQL uses the open source model Qwen2.5-Coder-32B-Instruct, and achieves an accuracy of 67.47% on the BIRD development set and 88.9% on the Spider test set, which is significantly better than other methods using open source models, and even exceeds several methods using the GPT-4o closed-source model. At the same time, BASE-SQL is easy to implement and highly efficient (on average, only five calls to the large language model are required to generate SQL once). The code will be open sourced at https://github.com/CycloneBoy/base_sql.

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