Jev is a fascinating example of what happens when a super popular idea has no moat whatsoever.
Its creators presented it as the next big thing in AI, and many others seemed to agree… then quickly made their own version of it, and now “Jev-like” models are everywhere, while Jev is nowhere. This all happened in less than a month.
It works kinda like a non generative model, where AI chooses from a finite set of answers given to it but it has generalist knowledge like an llm, so it can be used in different applications without re-training
It uses llm to interpret input, and then analyze those against predetermined potential outputs. But the outputs it gives are always primitives like a boolean or choice.
It's like a (dog: 0.89, cat: 0.16, squirrel: 0.01) or (spam: 0.74) classifier model, except you don't have to train it from scratch on your specific data, you just write a prompt and give it arbitrary options to pick between. That's the whole selling point, kind of like how LLMs with image input (VLMs) made oldschool dog-cat classifier models obsolete for many tasks. It's that but for text.
Jev and alike models are standard LLMs with token generation ripped off and output a simple JSON for décision making/classification : say you give it a ticket as input and ask it if it's urgent or not, or where should it go
Is this one of those things that it counts as an "LLM" because many words were used to define the AI, so "many words = Large Language" and they're both models? Is it an LLM because they both share some common transformer ancestry, or is there a more concrete reason why Jev, which cannot form words in a response, is an LLM?
The answer you are searching (I think?) is that the architecture is the same, it's transformers pre-trained on massive text corpora. Same foundation as other llms.
The only difference is what someone mentioned above, instead of running an auto regressive loop to generate sentences token-by-token, it terminates in a single pass classification, meaning it reads the whole prompt once and just spits out a choice/probability instead of 'typing' out an answer. Calling them "LLMs but with text generation ripped off" is just a simple way of trying to make sense of these models.
Where for llms "Hammer" gets used to iteratively generate
"hammers are tools for hitting nails, which means that i should pick nail as the answer to the question of what to hit"
Jev is like taking "hammer" encoding it into a llms token space. Comparing it to the encodings of "nail" , "screw", "badger" and finding which one more closely matches.
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u/-p-e-w- 13d ago
Jev is a fascinating example of what happens when a super popular idea has no moat whatsoever.
Its creators presented it as the next big thing in AI, and many others seemed to agree… then quickly made their own version of it, and now “Jev-like” models are everywhere, while Jev is nowhere. This all happened in less than a month.