One way: separately embed sender, recipients, subject, body - then use the embedding vectors as input to a logistic classifier
With that setup, I get 95% accuracy on email classification, training on 50-100 base examples. The model trains on CPU in under 1min, and it does inference in under 20ms (most of it is running the embeddings, so you can make it faster if you train your own embeddings model)
Here’s a gist with some sample code: https://gist.github.com/nicobrenner/056a5aaff5d0119c0032ecda...
That code applies the embeddings + classifier setup on the Banking77 dataset. It gets 93-94% accuracy depending on the embeddings you use (SOTA for this is ~95%, with much bigger and slower models)
You should use precision (when your model says “spam” how often is it spam?), recall (how many of the spam emails did it catch), or f1 (balanced between those two).
That model scales very well with quantities of requests.
For a moment I thought this was going to be a metaphor — maybe an ancient Chinese proverb about how paint brushes are made from horsehair and how you can't hold the horse to paint before you've turned the hair into a brush.
With Jev you each time pay for your prompt, you can't cache it.
It performed quite below Jev, but above other open decision models I tested (68 correct vs 79 correct for Jev - see [1]). I'm running it for the moderation benchmark as well, but that will probably take a few hours on my machine.
Ask Jeeves hired hundreds of cheap liberal arts majors to classify data, some users thought Jeeves was real, the stock spiked when big companies hired Jeeves to automate support thinking it was a silver bullet, and the whole thing collapsed when a better model came along, and it degenerated into ripping off rubes with bottom of the barrel ads.
just get an LLM to think and then force it to output a specific json with prefill post-think.
make sure to include good conditioning text in the prompt with examples of exactly what the output should be like. you don't want dissonance in the probabilities on the prefill.
The point isn't that new type of problem has been unlocked, rather a new approach that can unlock new use cases.
when you want a machine to reason about the prompt and generate a structured output not using an actual LLM makes no sense. I have been doing it since the first chain of thought open models became available.
perhaps there may be a way to get a Jev-type model to think for a very specific number of steps to gain control over its latency, if so that would be the next step. truncating LLM thinking like this does not work well, and its thinking isn't efficient anyway.
/a bit more digging and..
A Noul performs a Bernoulli trial—an experiment with exactly two outcomes (yes or no)—but instead of picking one, it returns the calibrated probability (ranging from 0.0 to 1.0) that the statement is true.
I hate it :)
Like, yeah, you don't hallucinate, but only because you force the user to decide in the end.
And that's...bad?
I think, it's a bit much to call this "no hallucinations".
Technically true, but in practice you could still choose the wrong result or the probabilities can be off.
In an analog circuit, maybe.
Like it or not, companies are going to use Jev unless you can offer something just as cheap and fast.
I wonder just how much of the business automation market, previously held by LLMs, is at risk here?
But funny that jev is getting its lunch eaten apparently in under two weeks?
I've used it to do web research where it follows the most appropriate links, decides what to record in state, etc. I struggle to see how you could implement something with a classifier. That said, I have no idea how deep the technology is and it might be replaced with open source pretty quickly since its drafting of the frontier models and the open source models seem almost as good.
I like the term decision model and I think it's warranted.
Yes, one general classifier would be very hard to train. However, you can create a sort of ensemble of classifiers, each trained in different tasks
I’m currently experimenting with this. So far I’ve combined classifiers for 13 different datasets, my target is 95 (the ones Laya used for training)
I guess it's not really a benchmark but you could say if it can do it faster it sort of could be taken as one.
soon I'll make sure that my home assistant pod answers to "Hey jeeves"
So why didn't they show both??