In recent years,with the rapid development of technologies such as big data and cloud computing,large-scale data ge-neration has deepened the dependence of various applications on database technology.However,traditional databases typically operate through the formalized database query language SQL,which poses a significant difficulty for users without programming or database usage experience,reducing the accessibility of databases across various fields.With the rapid advancement of artificial intelligence technologies like machine learning and deep neural networks,especially the surge of large language model technology sparked by the emergence of ChatGPT,there has been a profound synthesis and technological transformation of databases and in-telligent technology.Intelligent methods are employed to automatically translate user input language into SQL,meeting the opera-tional needs of database users of varying levels of expertise and enhancing databases'intelligence,environmental adaptability,and user-friendliness.To comprehensively focus on the latest research developments in intelligent SQL generation technology,this pa-per delves into three types of user inputs-example-based,text-based,and voice-based-and provides a detailed exposition of the re-search trajectory,representative works,and the latest advancements of various intelligent synthesis models.Additionally,this pa-per categorizes and compares the technical frameworks of these methods and provides an overall summary.Finally,it paper looks forward to future development directions in light of existing problems and challenges with current methods.
Database technologyIntelligent SQL synthesisSemantic parsingSQL syntaxLarge language models
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