  We apply foundation models to data discovery and exploration tasks.
Foundation models include large language models (LLMs) that show promising
performance on a range of diverse tasks unrelated to their training. We show
that these models are highly applicable to the data discovery and data
exploration domain. When carefully used, they have superior capability on three
representative tasks: table-class detection, column-type annotation and
join-column prediction. On all three tasks, we show that a
foundation-model-based approach outperforms the task-specific models and so the
state of the art. Further, our approach often surpasses human-expert task
performance. We investigate the fundamental characteristics of this approach
including generalizability to several foundation models and the impact of
non-determinism on the outputs. All in all, this suggests a future direction in
which disparate data management tasks can be unified under foundation models.
