From what I see, there may be two issues here. 1) LLM has data from a large set of sources, but in some cases it has very little data on certain sub-topics. W

From what I see, there may be two issues here. 1) LLM has data from a large set of sources, but in some cases it has very little data on certain sub-topics. With so few sources of data, it is easier for some weird corner of the internet to have conflicting data, and some LLM models may randomly favour that instead of the valid answer. So not hallucination in this case, instead, a wrong answer from wrong sources. 2) LLM doesn't know, but has seen from human behaviour that just "making shit up, when you don't know" is entirely acceptable for many humans. Here I'm not referring to people saying (as I am) "may be", but instead "No, it's this!" with immense surety, in order to win discussions. Combine this with 'temperature' settings many model interfaces have, and there you go. 2b) Alternatively, #2 is inline with "I believe in flying spaghetti monsters", and other beliefs such as astrology, ghosts, and endless other "there is zero proof, but humans have immense weird data on it". So #2 is solvable by changing temperature, or keeping temperature but saying "Don't make things up". #1 may be temp + random choice of a lower probability pathway. #2b, not sure what to think here. How will LLMs ingest data, and use it to determine what is acceptable logic processing, when a lot of stuff in category 2b has zero proof behind it. And with zero proof, people are absolutely positive of the status of said thing? #2b trains LLMs that "just having a random idea and then even killing people that don't agree, seems OK for humans". (think religious wars, violent acts, burning witches, etc)