Towards LLM-augmented Database Systems

Carsten Binnig

Recent LLMs such as GPT-4-turbo can answer user queries over multi-model data including tables and thus seem to be able to even replace the role of databases in decision-making in the future. However, LLMs have severe limitations since query answering with LLMs not only has problems such as hallucinations but also causes high-performance overheads even for small data sets. In this talk, I suggest a different direction where we use database technology as a starting point and extend it with LLMs where needed for answering user queries over multi-model data. This not only allows us to tackle problems such as the performance overheads of pure LLM-based approaches for multi-modal question-answering but also opens up other opportunities for database systems.

Carsten Binnig is a Full Professor in the Computer Science department at TU Darmstadt and a Visiting Researcher at the Google Systems Research Group. Carsten received his Ph.D. at the University of Heidelberg in 2008. Afterwards, he spent time as a postdoctoral researcher in the Systems Group at ETH Zurich and at SAP working on in-memory databases. Currently, his research focus is on the design of scalable data systems on modern hardware as well as machine learning for scalable data systems. His work has been awarded a Google Faculty Award, as well as multiple best paper and best demo awards.