| > What this points at is the abstraction/emergence crux of it all. Why does This paper has nothing to do with any questions starting with "why". It provides a metric for quantifying error on specific tasks. > If LLMs, as they are now, were comparable with human learning I think I missed the part where they need to be. > struggle to abstract all that training data to the point where outputting any frontend that deviates from the clearly used examples? ... a model such as GPT-5 trained on nearly all frontend code ever committed to any repo online, would have internalised more than that one template OpenAI predominantly leaned on There is a very big and very important difference between producing the same thing again and not being able to produce something else. When not given any reason to produce something else, humans also generate the same thing over and over. That's a problem of missing constraints, not of missing ability. Long before AI there was this thing called Twitter Bootstrap. It dominated the web for...much longer than it should have. And that tragedy was done entirely by us meatsacks (not me personally). Where there's no goal for different output there's no reason to produce different output, and LLMs don't have their own goals because they don't have any mechanisms for desire (we hope). [I've edited this comment for content and format] |
Ok, that's better than comparing LLMs to humans. ZSL however, has not proven anything of that sort false years ago, as it was mainly concerned with assessing whether LLMs are solely relying on precise instruction training or can generalise in a very limited degree beyond the initial tuning. That has never allowed for comparing human learning to LLM training.
Ironically, you are writing this under a paper that shows just that:
A model that cannot determine a short strings parity cannot have abstracted from the training data to arrive at the far more impressive and complicated maths challenges which it successfully solves in output. Some of the solutions we have seen in output require such innate understanding that, if there is no generalisation, far deeper than ZSL has ever shown, than this must come from training. Simple multiplication, etc. maybe, not the tasks people such as Easy Riders [0] throw at these models.
This paper shows exactly that even with ZSL, these models do only abstract in an incredibly limited manner and a lot of capabilities we see in the output are specifically trained, not generalised. Yes, generalisation in a limited capacity can happen, but no, it is not nearly close enough to yield some of the results we are seeing. I have also, neither here, nor in my initial comment, said that LLMs are only capable of outputting what their training data provides, merely that given what GPT-5 has been trained with, if there was any deeper abstraction these models gained during training, it'd be able to provide more than one frontend style.
Or to put it simpler, if the output provided can be useful for Maths at the Bachelor level and beyond and this capability is generalised as you believe, these tasks would not be a struggle for the model.
[0] https://www.youtube.com/@easy_riders