Afaik LLMs at their heart employ supervised training - as in, given a certain input-output distribution pairs in the dataset, it learns to recreate the given ou

Afaik LLMs at their heart employ supervised training - as in, given a certain input-output distribution pairs in the dataset, it learns to recreate the given outputs from the inputs. For all the inputs it hasn't seen, its output is essentially undefined, but hoping that the distribution of the novel inputs matches the outputs means that it will output truthful info. When it doesn't, it hallucinates. Since LLMs cannot distinguish the difference between the predicted distribution and the real one, it cannot be told to not hallucinate, in one pass. What actually could work is getting the output of a given LLM, and feeding it into another one to try and check for divergences to check for hallucinations.