How is this different from the categorisation models from ML era?
I'm skeptical of the quality of probability calibration for models if you aren't giving them training data. The issue is that there is a prior distribution that's unique to your specific data and the calibration is really sensitive to that.
Before now you had to train a model on your specific classification problem, now these new models don't require any specific training at all to do pretty well on novel problems.
There are classes of problem where this shifts the economics from "paying a human to do this is cheaper than AI" to "the software is now cheaper than the human"
I've asked twice now about what I'm missing and for a specific use case where you can't just do this with a regular LLM call and nobody has replied that so if you have the answer that would be great. Looking for something specific instead of just it's faster or cheaper which is definitely nice but I'm just not seeing what this opens up that was not previously possible
Yes, you could ask an LLM to choose between 4 options, and sure have it include a confidence score. But at the end of the day it's still LLM-ing and outputting tokens based on the total probability of the chain of tokens that precedes it to figure out what's most likely to come next.
This taps into a lower level of the model, skipping that tedious token generation step, and exposes you to more raw output from the model, e.g. the actual probabilities involved in answering the question.
It still needs to "think like an LLM" to interpret the question, so it's in the same family, but the thought process gets short-circuited once the question is understood and the raw weights are then extracted as pure, weighted probabilities.
Why would you want to use it? What does it enable beyond a traditional LLM?
(a) It's much more efficient (read: cheaper and faster), and not by a %, but an order of magnitude or two. This makes it practical to be used when an LLM isn't, due to its speed and accuracy, which opens up new use cases.
(b) By getting the raw options and weights, you're not getting the LLM's "spin" on the output, nor hallucination risk — instead, you're using an MRI to pull out exactly what it's thinking before the bullshit verbalization engine kicks in.
Does that help? Please correct me if I'm wrong or went too far in simplifying it, but that's my high-level understanding. I hope this helps.
For instance if you have a predictive model for market prices that is not calibrated that's... nice. If you have a calibrated model you can add a Kelly better and you have a trading strategy that makes money. Similarly if you are classifying articles or images or other contents to make a feed you might believe that 70% or 95% or some other level of precision is "good enough" and you can set the knob and turn on the cruise control.
And theoretically will give you better answers statistically as it's calibrated.