Can Foundation Models Wrangle Your Data?

TL;DR

This paper investigates the ability of large foundation models to perform classical data tasks like cleaning and integration, demonstrating they can achieve state-of-the-art results without task-specific training.

Contribution

It introduces a novel approach of framing data cleaning and integration as prompting tasks and evaluates foundation models' performance on these tasks.

Findings

Large FMs achieve state-of-the-art performance on data tasks

FMs generalize well without task-specific finetuning

Identifies challenges with private and domain-specific data

Abstract

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.