The example flags a '10 tablets' dose as a possible erroneous extraction, when the text says exactly that. So it is a false positive.
Furthermore, even if your goal was to catch logical errors rather than extraction errors, you are better off using programmatic checks on the extracted value and having a dedicated error handling path including additional full LLM calls, rather than decision model.
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.
The example flags a '10 tablets' dose as a possible erroneous extraction, when the text says exactly that. So it is a false positive.
Furthermore, even if your goal was to catch logical errors rather than extraction errors, you are better off using programmatic checks on the extracted value and having a dedicated error handling path including additional full LLM calls, rather than decision model.