Large Language Models (LLMs) in Data Management 7.5 credits

About the course

This course investigates how large language models (LLMs) might contribute to solving long standing problems in data management. Specifically it addresses three related questions:

1. How can LLMs assist in building chat interfaces over SQL databases?
2. How can LLMs assist in the conceptual modelling and definition of SQL databases?
3. How can LLMs assist in data integration where multiple databases are made interoperable?

Since these questions can only be addressed after understanding LLMs and basic data management, the first half of the course is devoted to covering these concepts. This starts with feed forward neural networks, RNNs, LSTMs and Seq2Seq models. Then we cover Transformers, BERT and GPT. After this we quickly review the main concepts in data management including conceptual modelling via entity relationship diagrams (ERDs), basic SQL and typical architectures. After covering basic concepts, the second half of the course turns to the three questions above of how LLMs might address long standing data management problems. This includes direct few shot approaches, approaches based on LangChain and vector databases and finally retrieval-augmented generation (RAG) approaches. Additional approaches may also be covered.