  Can foundation models (such as ChatGPT) clean your data? In this proposal, we
demonstrate that indeed ChatGPT can assist in data cleaning by suggesting
corrections for specific cells in a data table (scenario 1). However, ChatGPT
may struggle with datasets it has never encountered before (e.g., local
enterprise data) or when the user requires an explanation of the source of the
suggested clean values. To address these issues, we developed a retrieval-based
method that complements ChatGPT's power with a user-provided data lake. The
data lake is first indexed, we then retrieve the top-k relevant tuples to the
user's query tuple and finally leverage ChatGPT to infer the correct value
(scenario 2). Nevertheless, sharing enterprise data with ChatGPT, an externally
hosted model, might not be feasible for privacy reasons. To assist with this
scenario, we developed a custom RoBERTa-based foundation model that can be
locally deployed. By fine-tuning it on a small number of examples, it can
effectively make value inferences based on the retrieved tuples (scenario 3).
Our proposed system, RetClean, seamlessly supports all three scenarios and
provides a user-friendly GUI that enables the VLDB audience to explore and
experiment with the system.
