Trustworthy and Efficient LLMs Meet Databases
It targets database researchers and practitioners to reduce unfamiliarity with LLMs and inspire collaboration, but it is incremental as it focuses on sharing existing concepts rather than introducing new methods.
This tutorial addresses the problem of making large language models (LLMs) more trustworthy and efficient to reduce hallucinations and meet inference demands, by exploring efforts to integrate LLMs with databases and highlighting opportunities and challenges at their intersection.
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.