EPI-SQL: Enhancing Text-to-SQL Translation with Error-Prevention Instructions

arXiv:2404.14453v1 Announce Type: new
Abstract: The conversion of natural language queries into SQL queries, known as Text-to-SQL, is a critical yet challenging task. This paper introduces EPI-SQL, a novel methodological framework leveraging Large Language Models (LLMs) to enhance the performance of Text-to-SQL tasks. EPI-SQL operates through a four-step process. Initially, the method involves gathering instances from the Spider dataset on which LLMs are prone to failure. These instances are then utilized to generate general error-prevention instructions (EPIs). Subsequently, LLMs craft …

abstract arxiv conversion cs.ai cs.cl cs.db error framework language language models large language large language models llms natural natural language natural language queries novel paper performance prevention process queries sql sql queries tasks text text-to-sql through translation type