Determinism: If a model is given the exact same request/prompt twice, its two responses will also be identical. Whether or not the consistent response qualifies as correct.
The two concepts are very different.
(Ambiguous vs. precise prompt) x (Deterministic vs. Non-deterministic model) = 4 different scenarios.
A model itself can be non-deterministic without being ambiguous. If you know exactly how it functions, why it is non-deterministic (batch sensitive for instance), that is not an ambiguous model. Its operation is completely characterized. But it is non-deterministic.
An ambiguous model would simply be model whose operation was not characterized. A black box model for instance. A black box model can be deterministic and yet ambiguous.
Ambiguity is what happens when you change the prompt slightly, e.g. by adding a word: "Give an example of a single dice roll". Now as a human our expectation would be that this is the same question and should thus (in a deterministic system) receive the same answer. But to an LLM it may not be.
Yes, and thanks. That was my intended point - but you point out a better example. Slightly different prompts may also produce highly varied responses.
(My subsequent comments on ambiguous models was in case I was misinterpreting the comment I was replying to. I also generally think of ambiguity as a property of input. Either way, ambiguity is not the same as non-deterministic.)
A perfectly acceptable answer.
If it answers 1 every time it's still a perfectly acceptable answer.