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Text-to-SQL allows experts to use databases without in-depth knowledge ofthem. However, real-world tasks have both query and data ambiguities. Mostworks on Text-to-SQL focused on query ambiguities and designed chat interfacesfor experts to provide clarifications. In contrast, the data managementcommunity has long studied data ambiguities, but mainly addresses errordetection and correction, rather than documenting them for disambiguation indata tasks. This work delves into these data ambiguities in real-worlddatasets. We have identified prevalent data ambiguities of value consistency,data coverage, and data granularity that affect tasks. We examine howdocumentation, originally made to help humans to disambiguate data, can helpGPT-4 with Text-to-SQL tasks. By offering documentation on these, we foundGPT-4's performance improved by 28.9%.

Data Ambiguity Strikes Back: How Documentation Improves GPT's Text-to-SQL