Text-to-SQL with Large Language Models: DIN-SQL Approach
The DIN‑SQL approach enhances Text‑to‑SQL performance by using large language models in a decomposed in‑context learning framework with schema linking, query classification, SQL generation, and self‑correction modules, achieving state‑of‑the‑art 85.3% execution accuracy on the Spider benchmark by breaking complex queries into manageable sub‑tasks.
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.
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