LLM doesn't have to be non-deterministic, it can work just like any other deterministic algorithm.
But I am not sure why the insistence on the relevance of (non)determinism, rather than on the chaotic relation of the output to the input (which is true for both compilers and LLMs). In practice, inputs to the LLM, as well as to the compiler, change. And the fact is, the output can change radically due to that.
I think nobody really sends the same prompt twice to the LLM, so nobody cares about it being deterministic. I think what you're looking for is something different, some form of stability (as opposed to chaotic behavior). Although it's hard to define exactly, because in case of LLMs theory lacks behind praxis. (And as I said - we already gave up on stability with respect to performance by using compilers. We resolve that issue by doing performance testing.)
(I asked AI what's the opposite of "chaotic", I use "stable", but it seems that people use "deterministic" or "predictable" also in that meaning. So if you're using "deterministic" in that meaning, then you don't really care about sampling and temperature, i.e. determinism in the philosophical sense, but rather whether the output is consistent, albeit expressed differently.)
The whole point of technology is about control and consistency. Even with random parameters, we want their value to an item in a specific sets. When I use a tool, I want it to produce the outcome I want, not any other outcome it wants to produce. If it fails at that, it’s a defective tool.
But I am not sure why the insistence on the relevance of (non)determinism, rather than on the chaotic relation of the output to the input (which is true for both compilers and LLMs). In practice, inputs to the LLM, as well as to the compiler, change. And the fact is, the output can change radically due to that.
I think nobody really sends the same prompt twice to the LLM, so nobody cares about it being deterministic. I think what you're looking for is something different, some form of stability (as opposed to chaotic behavior). Although it's hard to define exactly, because in case of LLMs theory lacks behind praxis. (And as I said - we already gave up on stability with respect to performance by using compilers. We resolve that issue by doing performance testing.)
(I asked AI what's the opposite of "chaotic", I use "stable", but it seems that people use "deterministic" or "predictable" also in that meaning. So if you're using "deterministic" in that meaning, then you don't really care about sampling and temperature, i.e. determinism in the philosophical sense, but rather whether the output is consistent, albeit expressed differently.)