LLM-Enhanced Data Management
Xuanhe Zhou Xinyang Zhao Guoliang Li
Machine learning (ML) techniques for optimizing data management problems have
been extensively studied and widely deployed in recent five years. However
traditional ML methods have limitations on generalizability (adapting to
different scenarios) and inference ability (understanding the context).
Fortunately, large language models (LLMs) have shown high generalizability and
human-competitive abilities in understanding context, which are promising for
data management tasks (e.g., database diagnosis, database tuning). However,
existing LLMs have several limitations: hallucination, high cost, and low
accuracy for complicated tasks. To address these challenges, we design LLMDB,
an LLM-enhanced data management paradigm which has generalizability and high
inference ability while avoiding hallucination, reducing LLM cost, and
achieving high accuracy. LLMDB embeds domain-specific knowledge to avoid
hallucination by LLM fine-tuning and prompt engineering. LLMDB reduces the high
cost of LLMs by vector databases which provide semantic search and caching
abilities. LLMDB improves the task accuracy by LLM agent which provides
multiple-round inference and pipeline executions. We showcase three real-world
scenarios that LLMDB can well support, including query rewrite, database
diagnosis and data analytics. We also summarize the open research challenges of
LLMDB.