Text-to-SQL with Large Language Models: DIN-SQL Approach

This article discusses the DIN-SQL method for improving Text-to-SQL tasks using large language models (LLMs). It addresses challenges in traditional Seq2Seq approaches and proposes a decomposed in-context learning framework with self-correction modules. The method achieves state-of-the-art performance on the Spider dataset by breaking down complex queries into sub-tasks and leveraging LLM capabilities.

The approach includes four modules: schema linking, query classification/decomposition, SQL generation, and self-correction. Experiments show significant improvements in execution accuracy (85.3% on Spider) compared to previous methods. The article also covers indicator systems, SQL structure mapping, and best practices for indicator management in data analysis.