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Yes, but determinism != ambiguity, because determinism means: for this exact input the same exact output needs to follow.

If I ask the same model the same question I should be able to deterministically get the same answer.

Now if we phrase the same question slightly differently we would expect to get a slightly different answer.

by Jensson17 minutes ago|

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> Now if we phrase the same question slightly differently we would expect to get a slightly different answer.

You wouldn't get this from an LLM though, a tiny change in starting point gets a massive change in output, its a chaotic system.

by riazrizvi1 hours ago|

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Me: What’s an example of a dice roll?

LLM: 1

“Language ambiguity with determinism”? Sure I can juxtapose the terms but if it’s semantically inconsistent, then what we mean by that is not a deterministic, definitive thing. You’re chasing your tail on this ‘goal’.

by skybrian2 minutes ago|

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If you really want that to work while being reproducible, maybe give it a random number tool and set the seed?

by Nevermark33 minutes ago|

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Ambiguity: The request/prompt leaves a lot of room for interpretation. Many qualitatively different answers may be correct, relative to the prompt. Different or non-deterministic models will return highly variance results.

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.