This is probably better phrased as "LLMs may not provide consistent answers due to changing data and built-in randomness."
Barring rare(?) GPU race conditions, LLMs produce the same output given the same inputs.
dekhn from a decade ago cared a lot about stable outputs. dekhn today thinks sampling from a distribution is a far more practical approach for nearly all use cases. I could see it mattering when the false negative rate of a medical diagnostic exceeded a reasonable threshold.
Theorizing about why that is: Could it be possible they can't do deterministic inference and batching at the same time, so the reason we see them avoiding that is because that'd require them to stop batching which would shoot up costs?
A fixed seed is enough for determinism. You don't need to set temperature=0. Setting temperature=0 also means that you aren't sampling, which means that you're doing greedy one-step probability maximization which might mean that the text ends up strange for that reason.
> The non-determinism at temperature zero, we guess, is caused by floating point errors during forward propagation. Possibly the “not knowing what to do” leads to maximum uncertainty, so that logits for multiple completions are maximally close and hence these errors (which, despite a lack of documentation, GPT insiders inform us are a known, but rare, phenomenon) are more reliably produced.