Self-Contrastive Loop of Thought Method for Text-to-SQL Based on Large Language Model

Text-to-SQL is a task with excellent prospects and challenges, and it aims to convert natural language queries (NL) into corresponding structured query language (SQL) statements.The main challenge of this task is how to efficiently transform unstructured data and structured data.In recent years, the emergence of large language models (LLMs) has further promoted the development of this field.However, current LLM-based text-to-SQL methods rely on specific few-shot example construction, resulting in poor performance across domains.To solve this problem, we propose a text-to-SQL method of self-contrastive loop of thought structure.This method designs the LLM inference process as a loop structure based on the comparison of positive and negative examples.The model optimizes the generated results through continuous verification and error correction, greatly improving accuracy and reducing dependence on few-shot example construction.The experimental results on SPIDER and BIRD datasets show that this method can generate SQL with higher precision without relying on few-shot example construction.