and that wouldn't eliminate hallucinations just tell you if large details have likely been hallucinated.
But it's a method some research has used.
P.S. Also aren’t LLMs deterministic if you set their “temperature” to zero? Are there drafts if the temperature is zero? If not, then that’s the same as removing the randomness no?
>Just do the comparison on the user’s machine if the LLM provider is that cheap.
This is not possible. Users don't have the resources to run these gigantic models. LLM inference is not cheap. Open ai, Google aren't running profit on free cGPT or Bard.
>P.S. Also aren’t LLMs deterministic if you set their “temperature” to zero? Are there drafts if the temperature is zero? If not, then that’s the same as removing the randomness no?
It's not a problem of randomness. a temp of 0 doesn't reduce hallucinations. LLMs internally know when they are hallucinating/taking a wild guess. randomness influences how that guess manifests each time but the decision to guess was already made.
I never said it did.
> LLMs internally know when they are hallucinating/taking a wild guess.
No they don’t. If they did we would be able to program them to not do so.
I would argue that wild guesses are all LLMs are doing. They practically statistically guess their way to an answer. It works surprisingly well a lot of the time but they don’t really understand why they are right/wrong.
P.S. LLMs are kind of like students who didn’t study for the test so they use “heuristics” to guess the answer. If the test setter is predictable enough, the student might actually get a few right.
