[energy123]

Greedy decoding gives you that guarantee (determinism). But I think you'll find it to be unhelpful. The output will still be wrong the same % of the time (slightly more, in fact) in equally inexplicable ways. What you don't like is the black box unverifiable aspect, which is independent of determinism.

[willj]

If you’re using a model from a provider (not one that you’re hosting locally), greedy decoding via temperature = 0 does not guarantee determinism. A temperature of 0 doesn’t result in the same responses every time, in part due to floating-point precision and in part to to lack of batch invariance [1]

[1] https://thinkingmachines.ai/blog/defeating-nondeterminism-in...

[layer8]

What people don’t like is that the input-output relation of LLMs is difficult, if not impossible, to reason about. While determinism isn’t the only factor here (you can have a fully deterministic system that is still unpredictable in practical terms), it is still a factor.

[trklausss]

The question is: if we keep the same context and model, and the same LLM configuration (quantization etc.), does it provide the same output at same prompt?

If the answer is no, then we cannot be sure to use it as a high-level language. The whole purpose of a language is providing useful, concise constructs to avoid something not being specified (undefined behavior).

If we can't guarantee that the behavior of the language is going to be the same, it is no better than prompting someone some requirements and not checking what they are doing until the date of delivery.