It may not be as stupid as it sounds.
Randomising LLM outputs (temperature) results is outputs that will always have some degree of hallucination.
That’s just math. You can’t mix a random factor in and magically expect it to not exist. There will always be p(generates random crap) > 0.
However, in any probabilistic system, you can run a function k times and you’ll get an output distribution that is meaningful if k is high enough.
3 is not high enough.
At 3, this is stupid; all you’re observing is random variance.
…but, in general, running the same prompt multiple times and taking some kind of general solution from the distribution isn’t totally meaningless, I guess.
The thing with LLMs is they scale in a way that actually allows this to be possible, in a way that scaling with humans can’t.
… like the monkeys and Shakespeare, there probably a limit to the value it can offer; but it’s not totally meaningless to try it.
Randomising LLM outputs (temperature) results is outputs that will always have some degree of hallucination.
That’s just math. You can’t mix a random factor in and magically expect it to not exist. There will always be p(generates random crap) > 0.
However, in any probabilistic system, you can run a function k times and you’ll get an output distribution that is meaningful if k is high enough.
3 is not high enough.
At 3, this is stupid; all you’re observing is random variance.
…but, in general, running the same prompt multiple times and taking some kind of general solution from the distribution isn’t totally meaningless, I guess.
The thing with LLMs is they scale in a way that actually allows this to be possible, in a way that scaling with humans can’t.
… like the monkeys and Shakespeare, there probably a limit to the value it can offer; but it’s not totally meaningless to try it.