The paper "Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM" addresses the challenge of generating accurate SQL queries from user questions by leveraging the capabilities of large language models (LLMs). The authors propose a framework called Knowledge-to-SQL, which employs a Data Expert Large Language Model (DELLM) to provide helpful knowledge for text-to-SQL models. DELLM includes a table reading module and a fine-tuning process to generate expert knowledge. The framework also introduces a Preference Learning via Database Feedback (PLDBF) strategy to refine the generated knowledge, ensuring it aids in accurate database execution and precise SQL generation. Extensive experiments on the BIRD and Spider datasets demonstrate that DELLM enhances the performance of state-of-the-art text-to-SQL models, improving execution accuracy and valid efficiency scores. The paper highlights the significance of expert knowledge in bridging the gap between user questions and database schema, and provides a detailed implementation of the proposed framework, along with open-source code for further research.The paper "Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM" addresses the challenge of generating accurate SQL queries from user questions by leveraging the capabilities of large language models (LLMs). The authors propose a framework called Knowledge-to-SQL, which employs a Data Expert Large Language Model (DELLM) to provide helpful knowledge for text-to-SQL models. DELLM includes a table reading module and a fine-tuning process to generate expert knowledge. The framework also introduces a Preference Learning via Database Feedback (PLDBF) strategy to refine the generated knowledge, ensuring it aids in accurate database execution and precise SQL generation. Extensive experiments on the BIRD and Spider datasets demonstrate that DELLM enhances the performance of state-of-the-art text-to-SQL models, improving execution accuracy and valid efficiency scores. The paper highlights the significance of expert knowledge in bridging the gap between user questions and database schema, and provides a detailed implementation of the proposed framework, along with open-source code for further research.