171–180 of 378 posts
Earlier quoted context omitted.
I'll grant that you can guarantee the length of the output and, being a computer program, it's possible (though not always in practice) to rerun and get the same result each time, but that's not guaranteeing anything about said output.
You can guarantee what you have test coverage for :)
> after using it for months you get a ‘feel’ for what kind of mistakes it makes Sure, go ahead and bet your entire operation on your intuition of how a non-deterministic, constantly changing black box of software "behaves". Don't see how that could backfire.
Anyways, try a point release upgrade of a SOTA model, you're probably holding it wrong.
Earlier quoted context omitted.
Language models are deterministic unless you add random input. Most inference tools add random input (the seed value) because it makes for a more interesting user experience, but that is not a fundamental property of LLMs. I suspect determinism is not the issue you mean to highlight.
Sort of. They are deterministic in the same way that flipping a coin is deterministic - predictable in principle, in practice too chaotic. Yes, you get the same predicted token every time for a given context. But why that token and not a different one? Too many factors to reliably abstract.
I've found that 'not'[0] isn't something that LLMs can really understand.
Like, with us humans, we know that if you use a 'not', then all that comes after the negation is modified in that way. This is a really strong signal to humans as we can use logic to construct meaning.
But with all the matrix math that LLMs use, the 'not' gets kinda lost in all the other information.
I think this is because with a modern LLM you're dealing with billions of dimensions, and the 'not' dimension [1] is just one of many. So when you try to do the math on these huge vectors in this space, things like the 'not' get just kinda washed out.
This to me is why using a 'not' in a small little prompt and token sequence is just fine. But as you add in more words/tokens, then the LLM gets confused again. And none of that happens at a clear point, frustrating the user. It seems to act in really strange ways.
[0] Really any kind of negation
[1] yeah, negation is probably not just one single dimension, but likely a composite vector in this bazillion dimensional space, I know.
> after using it for months you get a ‘feel’ for what kind of mistakes it makes Sure, go ahead and bet your entire operation on your intuition of how a non-deterministic, constantly changing black box of software "behaves". Don't see how that could backfire.
So like every software? Why do you think there are so many security scanners and whatnot out there? There are millions of lines of code running on a typical box. Unless you're in embedded, you have no real idea what you're running.
Congrats on discovering what "thinking" models do internally. That's how they work, they generate "thinking" lines to feed back on themselves on top of your prompt. There is no way of separating it.
If you think that confusing message provenance is part of how thinking mode is supposed to work, I don't know what to tell you.
This is an illusion the chat UX concocts. Behind the scenes the tokens aren't tagged or colored.
Earlier quoted context omitted.
whatever happened to the system prompt buffer? why did it not work out?
because it's a separate context window, it makes the model bigger, that space is not accessible to the "user". And the "language understanding" basically had to be done twice because it's a separate input to the transformer so you can't just toss a pile of text in there and say "figure it out". so we are currently in the era of one giant context window.