Comment by qudat

Previous classification models need to be trained on the specific question/choices you are trying to output. Jev doesn't need to be retrained for every choice set provided.

LLMs can act as classifiers but they still have to generate text output in the form of a JSON object. This means they have to generate every single curly bracket, quote, command, etc. This turns out to be pretty expensive. On the other hand, Jev uses a different decision head so it doesn't generate text output at all, it outputs logits *only* for the choices provided. So it completely avoids the need to generate text at all, which means no malformed JSON and it's much faster as a result.

Finally, Jev also provides confidence scores that are actually reliable (not made up like LLMs).