Predicting Metadata for Humanitarian Datasets Using GPT-3

Responding to humanitarian disasters quickly, better still, anticipating them can save lives [1]. Data is key to this, not just having lots of data, but clean data which is well understood [2] in order to create a clear view of the situation on the ground. In many cases this critical data is stored in hundreds of small spreadsheets, so piecing them altogether can be time consuming and difficult to maintain as new data comes in during a humanitarian incident. Automating the process of data discovery would potentially speed responses and improve outcomes for affected people. One way to make discovery easier is to ensure that tabular data has metadata describing each column.