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In this paper, we propose Multi-Modal Databases (MMDBs), which is a new classof database systems that can seamlessly query text and tables using SQL. Toenable seamless querying of textual data using SQL in an MMDB, we propose toextend relational databases with so-called multi-modal operators (MMOps) whichare based on the advances of recent large language models such as GPT-3. Themain idea of MMOps is that they allow text collections to be treated as tableswithout the need to manually transform the data. As we show in our evaluation,our MMDB prototype can not only outperform state-of-the-art approaches such astext-to-table in terms of accuracy and performance but it also requiressignificantly less training data to fine-tune the model for an unseen textcollection.

Towards Multi-Modal DBMSs for Seamless Querying of Texts and Tables