1. they aren't, they are just popular online. 2. the author has nothing to do with the original comment. Why do you think academic reviews are double blind?

The sampling process isn't random. If you sample with identical sampling parameters and identical values for said parameters, you will always get same results. You only start getting "non deterministic" behavior when you start using more complex systems outside the scope of your control like multi gpu systems and batch processing. One llm sampled with cash prompting off and and batch processing off will always generate same results if all values are same.

It's possible to deterministically sample from a probability distribution. For example, just seed your RNG with a constant, or with the SHA256 hash of the context.

I suppose I have issues with the way "determinism" is used in the title of this article. It can mean different things to different people and in my mind stating that "Defeating Nondeterminism in LLM Inference" frames it as an actual issue with LLM inference. But its not, its an issue with LLM inference when you start using large scale inference with more complex parts such as systems which use multi gpu inference systems or batching processes and other mechanisms. It is not an issue when using an LLM without those more complex parts. Stating it this way muddies the signal and gives a false sense that this is a fundamental issue with architecture, where its an issue of the systems at scale...