11–20 of 95 posts
I don't think LLMs can self-correct without remembering their own training in some way.
Earlier quoted context omitted.
Is LeCun's Law even a thing? Searching up for it doesn't yield many results, except for a HN comment where it has a different definition. I guess it could be from some obscure paper, but with how poorly it's documented it seems weird to bring it up in this context.
I think the OP may be referring to this slide that Yann LeCun has presented on several occasions: https://youtu.be/MiqLoAZFRSE?si=tIQ_ya2tiMCymiAh&t=901 To quote from the slide: * Probability e that any produced token takes us outside the set of correct answers * Probability that answer of length n is correct * P(correct) = (1-e)^n * This diverges exponentially * It's not fixable (without a major redesign)
I kind of oscillatory effect when the train of tokens move further and further out of the distribution of correct tokens.
LLMs have no direct recollection of the qualia of their own training. This is at least a major way that I self-correct myself: if I'm about to talk about something I know, I'll try and figure out how/why I know that thing and in so doing, try to gauge whether I actually know that thing, if I'm hallucinating, or if I actually heard it from a less than reliable source etc. I don't think LLMs can self-correct without re…
(If someone tries this and it works, I’m quitting my phd and going back to camp counseling)
It's a similar approach to OpenAI's o1 model ( it's not cited, but there's no available paper for o1). I don't see any mention of weight release unfortunately.
They might have done that for O1, but the bigger change is the "runtime train of thought" that once the model received the prompt and before giving a definitive answer, it "thinks" with words and readjusts at runtime.
At least that's my understanding from these two approaches, and if that's true, then it's not similar.
AFAIK, OpenAI been doing reinforcement learning since the first version of ChatGPT for all future models, that's why you can leave feedback in the UI in the first place.
Earlier quoted context omitted.
I think the OP may be referring to this slide that Yann LeCun has presented on several occasions: https://youtu.be/MiqLoAZFRSE?si=tIQ_ya2tiMCymiAh&t=901 To quote from the slide: * Probability e that any produced token takes us outside the set of correct answers * Probability that answer of length n is correct * P(correct) = (1-e)^n * This diverges exponentially * It's not fixable (without a major redesign)
Is this similar to the effect that I have seen when you have two different LLMs talking to each other, they tend to descend into nonsense ? A single error in one of the LLM's output and that then pushes the other LLM out of distribution. I kind of oscillatory effect when the train of tokens move further and further out of the distribution of correct tokens.
Is that really true? I'd expect that with high temperature values, but otherwise I don't see why this would happen, and I've experimented with pitting same models against each other and also different models against different models, but haven't come across that particular problem.
LLMs have no direct recollection of the qualia of their own training. This is at least a major way that I self-correct myself: if I'm about to talk about something I know, I'll try and figure out how/why I know that thing and in so doing, try to gauge whether I actually know that thing, if I'm hallucinating, or if I actually heard it from a less than reliable source etc. I don't think LLMs can self-correct without re…
It's a similar approach to OpenAI's o1 model ( it's not cited, but there's no available paper for o1). I don't see any mention of weight release unfortunately.
how is it similar?
Earlier quoted context omitted.
Is LeCun's Law even a thing? Searching up for it doesn't yield many results, except for a HN comment where it has a different definition. I guess it could be from some obscure paper, but with how poorly it's documented it seems weird to bring it up in this context.
I think the OP may be referring to this slide that Yann LeCun has presented on several occasions: https://youtu.be/MiqLoAZFRSE?si=tIQ_ya2tiMCymiAh&t=901 To quote from the slide: * Probability e that any produced token takes us outside the set of correct answers * Probability that answer of length n is correct * P(correct) = (1-e)^n * This diverges exponentially * It's not fixable (without a major redesign)
Earlier quoted context omitted.
Is LeCun's Law even a thing? Searching up for it doesn't yield many results, except for a HN comment where it has a different definition. I guess it could be from some obscure paper, but with how poorly it's documented it seems weird to bring it up in this context.
I think the OP may be referring to this slide that Yann LeCun has presented on several occasions: https://youtu.be/MiqLoAZFRSE?si=tIQ_ya2tiMCymiAh&t=901 To quote from the slide: * Probability e that any produced token takes us outside the set of correct answers * Probability that answer of length n is correct * P(correct) = (1-e)^n * This diverges exponentially * It's not fixable (without a major redesign)
Humans make bad predictions all the time but we still seem to manage to do some cool stuff here and there.
part of an agents architecture will be for it to minimize e and then ground the prediction loop against a reality check.
making LLMs bigger gets you a lower e with scale of data and compute but you will still need it to check against reality. test time compute also will play a roll as it can run through multiple scenarios and "search" for an answer.