They use the OCR + structured data extraction in this example, but I'm thinking you could extrapolate this to any scenario where you are extracting some data (from a table, markdown file, OCRed doc), and then use a decision model to perform a fast on-the-fly review/validation.
My masters thesis used a similar approach a couple of years ago, we finetuned a BERT model to categorize natural language questions as either ambigous or non-ambigous in context of the database schema and which queries where plausible candidates to the question. It worked really well, I suppose JEV is orders of magnitude more powerful. There's going to be many applications of good decision models.
They use the OCR + structured data extraction in this example, but I'm thinking you could extrapolate this to any scenario where you are extracting some data (from a table, markdown file, OCRed doc), and then use a decision model to perform a fast on-the-fly review/validation.
My masters thesis used a similar approach a couple of years ago, we finetuned a BERT model to categorize natural language questions as either ambigous or non-ambigous in context of the database schema and which queries where plausible candidates to the question. It worked really well, I suppose JEV is orders of magnitude more powerful. There's going to be many applications of good decision models.