Comment by vgalin
9 hours ago
LLMs are non-deterministic, you could run the same prompt on the same code base with the same models, and have different result. I don't think only one run with each model is enough to draw conclusions.
9 hours ago
LLMs are non-deterministic, you could run the same prompt on the same code base with the same models, and have different result. I don't think only one run with each model is enough to draw conclusions.
We have to keep remembering this for some reason.
New model comes out and someone says, "it can (or can't) draw ducks on bikes" or some esoteric thing. But it's not enough statistical power to claim anything other than a personal anecdote.
I got a flat tire in downtown today. Therefore, driving in downtown causes flat tires.
Honest question about the non-deterministic nature of llms. Every programming language provides some way to control the random seed. Is this not true in GPU programming land?
It doesn't really matter for most purposes. Determinism doesn't make them any less chaotic, it's not like fixing the seed makes the output any more predictable. Some percentage of seeds will behave in ways you don't like for a particular input, and you can't know which beforehand.
Yes, but for one thing, floating-point math is non-associative so even if you use the exact same random seed, you can get different results on different implementation kernels.
For another, the dependence on a random seed is just one example of how you can get different behavior. You could just as well get different results due to e.g. a tiny difference in prompt wording or punctuation or whitespace.
If a model passes some test on 70% of random seeds, and fails on the other 30%, then knowing the rough probability of success is much more useful than knowing one random sample of the outcome.
And empirically this kind of probabilistic success is quite common, because many LLM benchmarks report "pass@k" success rates that are much higher than the "pass@1" success rate on a single attempt.
I don’t understand what “floating-point math is non-associative” has to do with the random selection of possible result token set after the soft max operation. The weights of the model are set and don’t change during the many matrix operations(which is deterministic regardless of the floating point encoding scheme).