LLMs solve the wrong problem (token prediction) very well. As a side effect, they solve the right problem (talking reasonably) at a decent level. Humans are more aligned with solving the right problem, and they are abominable at token prediction. It’s unsurprising that it takes no less than superhuman performance and ridiculous amount of data aimed at the wrong problem to incidentally end up OK on the right problem. And that when actually solving the right problem (as humans do), much less data would suffice.
What LLMs might manage at some point is leverage their understanding of human concepts to retarget the learning process a little bit closer to working on the right problem, something a bit more reasonable as a human-related inductive bias than bare token prediction. This might bring the sample efficiency with respect to the right problem up dramatically, and with it the resulting level of capability given the ridiculous amount of data.
LLMs solve the wrong problem (token prediction) very well. As a side effect, they solve the right problem (talking reasonably) at a decent level. Humans are more aligned with solving the right problem, and they are abominable at token prediction. It’s unsurprising that it takes no less than superhuman performance and ridiculous amount of data aimed at the wrong problem to incidentally end up OK on the right problem. And that when actually solving the right problem (as humans do), much less data would suffice.
What LLMs might manage at some point is leverage their understanding of human concepts to retarget the learning process a little bit closer to working on the right problem, something a bit more reasonable as a human-related inductive bias than bare token prediction. This might bring the sample efficiency with respect to the right problem up dramatically, and with it the resulting level of capability given the ridiculous amount of data.
(Though that’s probably not the current AGI bottleneck. Which I previously thought it was, but no longer.)