Bridging LLMs and Database Systems: A Deep Dive into Enhanced Relational Operators

Large language models (LLMs) are increasingly integrated into relational data processing via operator-like components—entity matchers, semantic filters, data imputers, and semantic rankers—that we call LLM-Enhanced Relational Operators (LROs). A growing number of systems and components have proposed diverse LROs, yet no unified view exists. This tutorial establishes a formal taxonomy along three dimensions—operating logic, operand granularity, and implementation variant—distinguishes standalone LRO components for data preparation from multi-LRO semantic query systems, covers optimization techniques (model cascades, semantic batching, cost-based planning), examines benchmark methodology, and discusses open challenges toward deeper LLM-database co-design.