MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation

TL;DR

This paper introduces MCS-SQL, a novel method that uses multiple prompts and multiple-choice selection to improve text-to-SQL generation with large language models, achieving state-of-the-art results on challenging benchmarks.

Contribution

The paper proposes a new approach leveraging multiple prompts and candidate query aggregation to enhance LLM-based text-to-SQL performance, especially on complex schemas.

Findings

Achieved 65.5% accuracy on BIRD benchmark

Achieved 89.6% accuracy on Spider benchmark

Established new state-of-the-art performance on BIRD

Abstract

Recent advancements in large language models (LLMs) have enabled in-context learning (ICL)-based methods that significantly outperform fine-tuning approaches for text-to-SQL tasks. However, their performance is still considerably lower than that of human experts on benchmarks that include complex schemas and queries, such as BIRD. This study considers the sensitivity of LLMs to the prompts and introduces a novel approach that leverages multiple prompts to explore a broader search space for possible answers and effectively aggregate them. Specifically, we robustly refine the database schema through schema linking using multiple prompts. Thereafter, we generate various candidate SQL queries based on the refined schema and diverse prompts. Finally, the candidate queries are filtered based on their confidence scores, and the optimal query is obtained through a multiple-choice selection that is presented to the LLM. When evaluated on the BIRD and Spider benchmarks, the proposed method achieved execution accuracies of 65.5\% and 89.6\%, respectively, significantly outperforming previous ICL-based methods. Moreover, we established a new SOTA performance on the BIRD in terms of both the accuracy and efficiency of the generated queries.