In the rapidly evolving AI era with large language models (LLMs) at the core,
making LLMs more trustworthy and efficient, especially in output generation
(inference), has gained significant attention. This is to reduce plausible but
faulty LLM outputs (a.k.a hallucinations) and meet the highly increased
inference demands. This tutorial explores such efforts and makes them
transparent to the database community. Understanding these efforts is essential
in harnessing LLMs in database tasks and adapting database techniques to LLMs.
Furthermore, we delve into the synergy between LLMs and databases, highlighting
new opportunities and challenges in their intersection. This tutorial aims to
share with database researchers and practitioners essential concepts and
strategies around LLMs, reduce the unfamiliarity of LLMs, and inspire joining
in the intersection between LLMs and databases.
