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)
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)
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.
My bots are all named Jeeves lol. I have a CLI tool I use that connects up to a LLM I made and I call it Jeeves too ...so funny. I really didn't use Jeeves all that much I tended to use...I think it was called Web crawler pre-google era
I just did a run with a benchmark I just used to test other models against. (It's about detecting irony in german soccer tweets). On my M5 Pro with 48GB it took over 30min to decide on just 100 tweets, the thinking definitely takes long.
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.
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.
A classifier is a subset of generative text, so I think responses like this miss the point. Jev is cheap enough and fast enough to sprinkle across your app in ways that LLM would be infuriatingly laggy and unnecessarily expensive, and it's never going to be injected to provide a sorting algorithm in python.
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.
Doesn't the fact that it's general purpose warrant a new term? It's partly that it doesn't need to be trained, but it's also able to play games based on game state, I'd imagine it would be hard to train a classifier to do something like this because you'd need to represent a good distribution of all the states. The general purpose llm world understanding underneath it allows for this.
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.
Just curious, where has this term 'noul' come from for yes/no ansers?
/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.
Not quite.. that boolean is about whether the voltage exceeds some threshold. It's not about how close the voltage is to the circuit's maximum possible threshold, or how much it exceeds the threshold.
Seems like this is the way, a hybrid approach where some of the pipeline will be jev like and some traditional LLM depending on the nature of the work.
interesting bench list, what about benchmark against smaller or bigger models? 9B looks too huge for small like laya, and too small for llm-level decisions.
If the model does autoregressive reasoning before the decision, doesn't that give up much of what a Jev-style model buys you (a single forward pass, cheap calibrated probabilities)? Or is the point mainly to keep the typed output and probability interface while getting better accuracy on harder cases?
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)
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.
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.
[1] https://tn1ck.com/blog/jevdit
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.
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.
/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.
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.
So why didn't they show both??
soon I'll make sure that my home assistant pod answers to "Hey jeeves"