Normal LLM will do the classification on the text that is generated. Jev just returns the classification and confidence.

It has the advantage of speed and the confidence not being hallucinated.

But LLMs start to generalise on the pattern, rather than the classification that you want the more examples you have to train on.

LLMs start to break down as well the more classifications you have. Laya (Open source paper Jev is based on) even mentions that over 20 classifications and it starts to fail rapidly.

20 is around the level of sentiment analysis or minor intent routing. There are cheaper, smaller and easier ML models for that level of classification.

Jev is just as non deterministic as any llm.

That is, if you force any llm to return json and a confidence it can also do that too and mostly likely it will he better at any one shot classification task than Jev.

LLMs have the great quality of knowing more due to the depth and richness of the training data. If Jev is trying to classify anything outside of its training data, it’s going to do a terrible job.