  Foundation Models (FMs) are models trained on large corpora of data that, at
very large scale, can generalize to new tasks without any task-specific
finetuning. As these models continue to grow in size, innovations continue to
push the boundaries of what these models can do on language and image tasks.
This paper aims to understand an underexplored area of FMs: classical data
tasks like cleaning and integration. As a proof-of-concept, we cast five data
cleaning and integration tasks as prompting tasks and evaluate the performance
of FMs on these tasks. We find that large FMs generalize and achieve SoTA
performance on data cleaning and integration tasks, even though they are not
trained for these data tasks. We identify specific research challenges and
opportunities that these models present, including challenges with private and
domain specific data, and opportunities to make data management systems more
accessible to non-experts. We make our code and experiments publicly available
at: https://github.com/HazyResearch/fm_data_tasks.
