alt Hacker NewsA lot of AI stuff is like that, actually. GPT 2 and 3 were mainly curiosities that would get articles on tech news sites but didn’t attract much attention from the general public. But after RLHF the UX improved dramatically and ChatGPT became mainstream. A lot of MLEs underestimate how much UX matters, and the same is true for Jev.
> how do these models calculate probabilities?
The short answer is: more and better data.
The long answer is: probably the same way any other classifier does. Take your ground truth probabilities and your predicted probabilities. The gap between those is called cross entropy, which is used to push the model towards the correct probabilities.
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From what I’ve gathered, it doesn’t work at all like a normal classifier. As in, for a classifier you’d train it for a specific task to reduce the cross entropy. So, in a hand-wavy way “the model knows the truth”. For Jev-type models it’s more like an LLM, in the sense that the “truth” is in the context you provide. But instead of producing a completion it fills in the schema. But it’s always - “given the context, the schema needs to be filled in like this”
as s.o. who registered their chatgpt account on the third day after they released, I can tell you chatgpt actually became mainstream overnight.
> A lot of AI stuff is like that, actually.
Chain of thought was that way. DeepSeek came out, and not long thereafter everyone had adopted chain of thought.
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> which is used to push the model towards the correct probabilities.
The definition of "correct probability" here is usually "p=1 for the ground truth label, p=0 for all others." Which seems like an intuitive approach but if you actually think about it doesn't trivially imply the model would learn sensible distributions under uncertainty.