Interesting that you just found this - I also referred back to it as a demonstration of a classifier just hours ago, and gave it a tweaking there.
I found in my initial experimentation that the AI just couldn’t interpolate between range(0.0:“boring and reliable”, 1.0: “unexpected and human”). Hence the table. Which GPT-3.5-turbo adhered to by almost direct selection instead if inference.
This is the kind of thing that could be rewritten 0-100, avoiding any mention of “temperature” so AI doesn’t use preconceived notions. Then a single-token output with logprobs could be weighted by the top-5 certainties.
For your concern of conflicts in inference goals, I didn’t instruct the AI which to favor.
We often consider this conflict ourselves: We wish to obtain wild and loose contents, but within stable reliable containers and predictable actions. The new response_format:json seems to address a bit of that for a particular case.