Francois Chollet

And the thing is, the way you measure performance is not a technical detail.

It's not an afterthought because it's going to narrow down the set of questions that you're asking.

And so accordingly, it's going to narrow down the set of answers that you're looking for.

If you look at the benchmarks we're using for LLMs, they're all memorization-based benchmarks, right?

Like sometimes they are literally just knowledge-based, like a school test.

And even if you look at the ones that are explicitly about reasoning, you realize if you look closely that in order to solve them, it's enough to memorize a finite set of reasoning patterns.

and then you just reapply them.

They're like static programs.

LLMs are very good at memorizing static programs, small static programs.

And they've got this sort of like bank of solution programs.

And when you give them a new puzzle, they can just fetch the appropriate program, apply it, and it's looking like it's reasoning, but really it's not doing any sort of on-the-fly program synthesis.

All it's doing is program fetching.

So you can actually solve all these benchmarks with memorization.

And so what you're scaling up here, like if you look at the models, they are big parametric curves fitted to a data distribution, which I call a descent.

So they're basically these big interpolative databases, interpolative memories.

And of course, if you scale up the size of your database and you cram into it more knowledge, more patterns and so on,

you are going to be increasing its performance as measured by a memorization benchmark.

That's kind of obvious.

But as you're doing it, you are not increasing the intelligence of the system one bit.

You are increasing the skill of the system.