by JumpCrisscross 4 hours ago
> I do not need the whole model. I don't need the model to have knowledge of the rain history of Algeria nor how many colors are in the Russian flag
Isn’t the performance gap between quantized and full models indicative that even if you aren’t using it directly, the model knowing the colors in the Russian flag does have something to do with the intelligence you demand?
Do quantized models specifically prune out specific knowledge? I think they just compress things down but they're still in there. You'd most likely need to do that when you're doing the initial model training, but I'm not expert.
> they just compress things down but they're still in there
The compression is almost certainly in part specific knowledge getting fuzzed.
Yeah, but it's everything getting fuzzed, including the parts you care about.
Quantizing is one thing. But in general it's self-evident that training the model on information that is irrelevant to your use case does not necessarily improve ability, otherwise you'd have AGI just from reinforcing your model on memorizing the first 10^50 digits of pi.
Likewise, LLMs do not violate the laws of information theory, and therefore the only way to encode X amount of information in Y amount of bits where X > Y is by performing what is effectively lossy compression, and as X grows larger relative to Y the compression ratio must change to lose ever more information.
Yes, for the sake of making chatbots that are "conversational" in that they can interpret natural language as input and produce code as output you can easily benefit in incidental and unintuitive ways by training it on more natural language text. But for a given fixed parameter size, it's possible to produce a better model for a specific task by selectively not muddying its training set in the first place with things that are likely irrelevant to the task.
> it's self-evident that training the model on information that is irrelevant to your use case does not necessarily improve ability
We don’t understand AI or natural intelligence well enough to make such statements. As for self evidence, cross-domain competence in humans and the rise of generalist models over domain-specific ones (on competence, not cost) seems to pretty directly tank your hypothesis.
>But in general it's self-evident that training the model on information that is irrelevant to your use case does not necessarily improve ability, otherwise you'd have AGI just from reinforcing your model on memorizing the first 10^50 digits of pi.
It's hardly self-evident, and your counter-example is hardly applicable.
The first 10^50 of pi is not the same as having BREADTH of information in the training data, which is the whole point not just any random "information that is irrelevant to your use case".
not to mention that the first 10^50 digits of pi compress to quite small formula, so not much information there to begin with from a shannon/kolmogorov perspective
It is self-evident. Bringing up Kolmogorov complexity is irrelevant, we're talking about rote memorization, but if you can't ignore the given example then replace "digits of pi" with "bits of output from a true random number generator". There's an infinite amount of information that we could shove into a model, and a finite amount of bits with which to store any of that information such that it can be usefully recalled or form useful logical associations.
Apparently irrelevant data can help because model weights are entangled.