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.)
It’s not clear to me that personality is completely separate from capabilities, especially with inference time reasoning.
Also, what do you mean by “bigger templates”?
Sorry, I meant “bigger sets of templates”. See here:
My intuition would be that models learn to implement more general templates as well.