For certain tasks, it seems much, much more efficient. That's not nothing. People have been using LLMs for various classification tasks.

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jackb4040 7 hours ago | parent | next [-]

In my company, and I think in most companies that are using AI at all, one of the first ways it got integrated is as a classifier, to tag orders based on feeding all their data into a prompt and asking for a structured output.

I think demand for tools that are more tailored for this type of integration is high. I don't really understand why Jev is supposed to get my company's decisions right more than an LLM, but regardless of the tech I think people are just excited about the possibility of iterating faster, more explainability, higher-level tools that are specifically created to help hone classifiers etc.

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linuxftw 7 hours ago | parent | prev [-]

Great, we don't need 15 thousands posts per hour across social media channels. We had classification NN before LLMs as well.

	▲	NeutralCrane 3 hours ago \| parent \| next [-]
		I think the point of Jev is to thread the needle of the gap between non-LLM classifiers and LLMs. Classifiers like classical NNs require: - annotated data, potentially a lot of it - training - inference #2 and #3 aren’t a big deal if you have an ML engineer, but #1 will always be a potential headache no matter who you are. The tradeoff is that they could be quite fast, cheap, and you can get probabilities, not just classes. With LLMs you get: - zero shot classification (no dataset or training required) - potentially can use third party model providers like OpenAI off the shelf. Don’t even need to host your own model. The downside to LLMs is that they are comparatively slow and expensive to traditional classifiers. Historically they also were prone to hallucination or malformed responses, though not as much these days. You also can technically get log-probs back, but these aren’t equivalent to the classifier probabilities. Jev gets you the zero-shot, zero-infra benefits of LLMs, while being closer to the speed and cost of traditional ML classifiers, as well as both classification and probability responses.
	▲	danielmarkbruce 7 hours ago \| parent \| prev [-]
		Yeah but they weren't that great, you couldn't ask for arbitrary classifications after the model was trained. You are underestimating what they've done here, even if it does seem a little overhyped.