Publikation
OmniscientDB: A Large Language Model-Augmented DBMS That Knows What Other DBMSs Do Not Know
Matthias Urban; Duc Dat Nguyen; Carsten Binnig
In: Rajesh Bordawekar; Oded Shmueli; Yael Amsterdamer; Donatella Firmani; Andreas Kipf (Hrsg.). Proceedings of the Sixth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management. International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM-2023), aiDM@SIGMOD 2023, June 18, Seattle, WA, USA, Pages 4:1-4:7, ISBN 979-8-4007-0193-1, ACM, 2023.
Zusammenfassung
In this paper, we present our vision of OmniscientDB, a novel database that leverages the implicitly-stored knowledge in large language models to augment datasets for analytical queries or even machine learning tasks. OmiscientDB empowers its users to augment their datasets by means of simple SQL queries and thus has the potential to dramatically reduce the manual overhead associated with data integration. It uses automatic prompt engineering to construct appropriate prompts for given SQL queries and passes them to a large language model like GPT-3 to contribute additional data (i.e., new rows, columns, or entire tables), augmenting the explicitly stored data. Our initial evaluation demonstrates the general feasibility of our vision, explores different prompting techniques in greater detail, and points towards several directions for future research.