Sorry, I meant “bigger sets of templates”. See here:
My model is that all LLM progress so far has involved making LLMs better at the “top-down” thing. They end up with increasingly bigger databases of template problems, the closest-match templates end up ever-closer to the actual problems they’re facing, their ability to fill-in the details becomes ever-richer, etc. This improves their zero-shot skills, and test-time compute scaling allows them to “feel out” the problem’s shape over an extended period and find an ever-more-detailed top-down fit.
But it’s still fundamentally not what humans do. Humans are able to instantiate a completely new abstract model of a problem – even if it’s initially based on a stored template – and chisel at it until it matches the actual problem near-perfectly. This allows them to be much more reliable; this allows them to keep themselves on-track; this allows them to find “genuinely new” innovations.
The two methods do ultimately converge to the same end result: in the limit of a sufficiently expressive template-database, LLMs would be able to attain the same level of reliability/problem-representation-accuracy as humans. But the top-down method of approaching this limit seems ruinously computationally inefficient; perhaps so inefficient it saturates around GPT-4′s capability level.
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(As an abstract analogy: imagine that you need to color the space bounded by some 2D curve. In one case, you can take a pencil and do it directly. In another case, you have a collection of cutouts of geometric figures, and you have to fill the area by assembling a collage. If you have a sufficiently rich collection of figures, you can come arbitrarily close; but the “bottom-up” approach is strictly better. In particular, it can handle arbitrarily complicated shapes out-of-the-box, whereas the second approach would require dramatically bigger collections the more complicated the shapes get.)
Sorry, I meant “bigger sets of templates”. See here:
My intuition would be that models learn to implement more general templates as well.