  Text-to-SQL allows experts to use databases without in-depth knowledge of
them. However, real-world tasks have both query and data ambiguities. Most
works on Text-to-SQL focused on query ambiguities and designed chat interfaces
for experts to provide clarifications. In contrast, the data management
community has long studied data ambiguities, but mainly addresses error
detection and correction, rather than documenting them for disambiguation in
data tasks. This work delves into these data ambiguities in real-world
datasets. We have identified prevalent data ambiguities of value consistency,
data coverage, and data granularity that affect tasks. We examine how
documentation, originally made to help humans to disambiguate data, can help
GPT-4 with Text-to-SQL tasks. By offering documentation on these, we found
GPT-4's performance improved by 28.9%.
