To take a simplistic example, because a human who can provide a long motivated solution to a math problem that you re-use every three years likely understands the math behind it, while an LLM providing the same solution is likely just copying it from the training set and would be fully unable to resolve a similar problem that did not appear in the training set.
Lots of exams are designed to prove certain knowledge given safe assumptions of the known limitations of humans, which are completely wrong for machines. The relative difficulty of rote memorization versus having an accurate domain model is perhaps the most obvious one, but there are others.
Also, the opposite problem will often exist - if the exam is provided in the wrong format to the AI, we may underestimate its abilities (i.e. a very similar prompt may elicit a significantly better response).
> Lots of exams are designed to prove certain knowledge given safe assumptions of the known limitations of humans, which are completely wrong for machines. The relative difficulty of rote memorization versus having an accurate domain model is perhaps the most obvious one, but there are others.
I don't think this is obvious at all. Sure, it's easy enough to make mechanistic arguments (after all, we don't even really understand most of the mechanics on either side, human and ai) but that doesn't mean it will matter in the slightest when we evaluate the outcome in regards to any metric we care about.
It's extremely obvious to anyone who works on real systems.
> (after all, we don't even really understand most of the mechanics on either side, human and ai)
We don't need mechanistic explanations to observe radical differences in behavior, and there are mechanistic explanations for some of these differences.
Eg, CNNs and the visual cortex. We really do understand some mechanisms -- of both CNNs and VCs -- well enough to understand divergences in failure modes. Adversarial examples, for example.
> Sure, it's easy enough to make mechanistic arguments, but that doesn't mean it will matter in the slightest when we evaluate the outcome in regards to any metric we care about.
I can't quite figure out what this sequence of tokens is supposed to mean.
Anyways, again, the failure modes of LLMs are obviously different than the failure modes of humans. Anyone who has spent even a trivial amount of time training both will instantly observe that this is true.
> Lots of exams are designed to prove certain knowledge given safe assumptions of the known limitations of humans, which are completely wrong for machines. The relative difficulty of rote memorization versus having an accurate domain model is perhaps the most obvious one, but there are others.
Lots of exams are designed to prove certain knowledge given safe assumptions of the known limitations of humans, which are completely wrong for machines. The relative difficulty of rote memorization versus having an accurate domain model is perhaps the most obvious one, but there are others.
Also, the opposite problem will often exist - if the exam is provided in the wrong format to the AI, we may underestimate its abilities (i.e. a very similar prompt may elicit a significantly better response).