RSL-SQL: Robust Schema Linking in Text-to-SQL Generation

TL;DR

RSL-SQL introduces a robust schema linking framework for Text-to-SQL tasks, improving recall and accuracy by combining bidirectional linking, contextual augmentation, and self-correction, achieving state-of-the-art results on benchmark datasets.

Contribution

The paper presents a novel RSL-SQL framework that enhances schema linking in Text-to-SQL generation through multiple strategies, significantly improving recall and accuracy over existing methods.

Findings

Achieves 94% recall in pattern linking with reduced input columns by 83%.

Attains state-of-the-art execution accuracy on BIRD (67.2%) and Spider (87.9%) benchmarks.

Outperforms GPT-4 based systems using cheaper models with the same prompts.

Abstract

Text-to-SQL generation aims to translate natural language questions into SQL statements. In Text-to-SQL based on large language models, schema linking is a widely adopted strategy to streamline the input for LLMs by selecting only relevant schema elements, therefore reducing noise and computational overhead. However, schema linking faces risks that require caution, including the potential omission of necessary elements and disruption of database structural integrity. To address these challenges, we propose a novel framework called RSL-SQL that combines bidirectional schema linking, contextual information augmentation, binary selection strategy, and multi-turn self-correction. We improve the recall of pattern linking using forward and backward pruning methods, achieving a strict recall of 94% while reducing the number of input columns by 83%. Furthermore, it hedges the risk by voting between a full mode and a simplified mode enhanced with contextual information. Experiments on the BIRD and Spider benchmarks demonstrate that our approach achieves SOTA execution accuracy among open-source solutions, with 67.2% on BIRD and 87.9% on Spider using GPT-4o. Furthermore, our approach outperforms a series of GPT-4 based Text-to-SQL systems when adopting DeepSeek (much cheaper) with same intact prompts. Extensive analysis and ablation studies confirm the effectiveness of each component in our framework. The codes are available at https://github.com/Laqcce-cao/RSL-SQL.