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

RetClean: Retrieval-Based Data Cleaning Using Foundation Models and Data Lakes