LLMs and Databases: A Synergistic Approach to Data Utilization

This technical report discusses the integration of Large Language Models (LLMs) with databases to enhance data utilization. It outlines the advancements in natural language to SQL (NL2SQL) translation, which has improved the accessibility of database interactions. The report details the challenges faced by LLMs, particularly in latency-sensitive regression problems, and introduces the development of pre-trained cardinality estimation models to address these issues. The authors explore innovative systems that leverage LLM capabilities to analyze both structured and unstructured data, facilitating on-demand information extraction. The document presents findings on schema linking for NL2SQL, emphasizing the importance of context in generating accurate SQL queries. It also discusses in-context learning as a technique to improve NL2SQL accuracy through example selection. The report concludes with insights on the future research directions for enhancing database performance through the synergy of LLMs and traditional database models.