Would setting the temperature to zero degrade the quality of response?
For an llm, a trivial change in wording could produce a big change in answer, same as running it again with a new random seed. "Prompt engineering" is basically overfitting if not approached methodically. For example, it would be interesting to try deliberate permutations of an input that don't change the meaning and see how the answer changes as part of an evaluation.
Look at this minimal implementation (Karpathy's) of LLaMA, the only randomness is in the "sample" function that comes in at non-zero temperature, otherwise its easy to see everything is deterministic: https://github.com/karpathy/llama2.c/blob/master/run.c
Otoh, with MoE like GPT-4 has, it can still vary at zero temperature.
I think this right here is the answer to measuring and comparing model performance.
Instead of trying to compare models holistically, we should be comparing them for specific problem sets and use cases... the same as we compare humans against one another.
Using people as an example, a hiring manager doesn't compare 2 people holistically, they compare 2 people based on how well they're expected to perform a certain task or set of tasks.
We should be measuring and comparing models discriminately rather than holistically.