There's a reason stochastic was used in the original phrase instead of "probabilistic."
While most inference executions are intentionally non-deterministic, even a purely deterministic one would still be stochastic in that the model itself was built in a process such that the statistical frequency, sequencing, etc of the training text and followup processes all heavily influence the result.
Because of that, the output is the sort of thing that is not expected to generate 100% perfect output 100% of the time, but to have a good probability of being like-in-kind-to-the-training-data (and useful/relevant as a result).
(As compared to a non-stochastic model, like arithmetic on integers, where 2+2 is always gonna be 4 and you don't have a chance of coming up with some novel pair of inputs to addition that will cause your arithmetic to miss the mark.)
Agreed. My point was to question the use of “just“ to obscure an incredibly complicated process, which has been shown repeatedly to rely on generalizations that are indistinguishable from world models.
Now, it is true that the world they’re modeling is the world of tokens. But insofar as those tokens, be they text or images or videos, are themselves modeling the real world, LLMs do have a model of the real world.
While most inference executions are intentionally non-deterministic, even a purely deterministic one would still be stochastic in that the model itself was built in a process such that the statistical frequency, sequencing, etc of the training text and followup processes all heavily influence the result.
Because of that, the output is the sort of thing that is not expected to generate 100% perfect output 100% of the time, but to have a good probability of being like-in-kind-to-the-training-data (and useful/relevant as a result).
(As compared to a non-stochastic model, like arithmetic on integers, where 2+2 is always gonna be 4 and you don't have a chance of coming up with some novel pair of inputs to addition that will cause your arithmetic to miss the mark.)