[skybrian]

LLM’s are too unpredictable for many practical uses so I’d guess better predictability is better. Hopefully the change the paper proposes will help!

But here’s a case for the other side: sure, most mistakes are just errors, but evolution happens via “mistakes.” Also, LLM’s often deliberately add add randomness at inference time.

[dartos]

> evolution happens via “mistakes.”

That’s a nice slogan, but it’s a gross oversimplification.

In the natural world, you can say that mistakes in DNA replication leads to evolution, but that’s discounting the entire process of natural selection.

Same with creativity. Look at Picasso. His was a technically brilliant realistic painter at 15, but his work later in life evolved to be more abstract and weird. I don’t think that was the result of mistakes, but rather intentionally breaking patterns he learned in his youth.

[skybrian]

To oversimplify, evolution is a generate-and-test process and the evaluation step is critical. Something needs to decide which variations are better. Often, with generative AI, it’s people who judge the results. Still, generating interesting examples (the brainstorming phase) plays some role in that.

I don’t know a whole lot about Picasso’s art, but I imagine the way he evaluated his own work played an important role, in being able to see that sometimes creative accidents are interesting.