Can Foundation Models Wrangle Your Data?

Understand

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.