> some specific thing you might want to do with the model.

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.

You could have two models answer 100 questions the same way, and differ on the 101st. They’re unpredictable by nature - if we could accurately predict them we’d just use the predictions instead.

(Stupid question) are models still non-deterministic if you set the temperature to zero?

Would setting the temperature to zero degrade the quality of response?

Some GPU operations give different results depending on the order they are done. This happens because floating point numbers are approximations and lose associativity. Requiring a strict order causes a big slowdown.

Even at T=0 and run deterministically, the answers still have "randomness" with respect to the exact prompt used. Change wording slightly and you've introduced randomness again even if the meaning doesn't change. It would be the same for a person.

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.

But if T=0 and you use the exact same input (not a single word or position changes) do you get the same output? Reading your response it implies that the randomness is related to even slight changes.

As a sibling comment mentioned, threading on a gpu is not automatically deterministic so you could randomness from there, although I can't think of anything in the forward pass of a normal LLM they would depend on execution order. So yes, you should get the same, it's basically just matrix multiplication. There may be some implementation details I don't know about that would add other variability though.

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.

Well the same is true for people, and yet hiring managers still available valuate for specific tasks.

Not mathematically, at the very least. Perplexity is a translation of the best measure we have for informing us how a model is doing empirically over a test dataset (both pre and post). It is enough to be, usably, at least the final word on how different quantization methods perform.

Subjective ratings are different, but for compression things are quite well defined.