They are lossy statistical prediction machines - to eliminate hallucinations effectively eliminates the lossy part and you might as well just use predicates in a database of facts.

Yeah, they only “proved” hallucination is inevitable by defining it to be any case where the llm doesn’t provide the “correct” answer. By this definition, an LLM deciding not to answer is also a “hallucination”.

My intuition on this is like training a classifier on four classes: dog, cat, cow and IDK. It feels intuitive to us but really hard to do in practice. In the classifier case, we are leveraging a subset of data to train the model to give correct answers to unseen data. If we want the model to generalize to unseen data we need it to call unseen dog-like things a dog. If not, then all unseen dogs would be IDK. Learning that boundary of "known vs unknown" is very hard. If done poorly, you have a model that cannot abstract to anything that is not in the dataset which is a huge part of what makes these models so impressive. I'm sure there is more to it than this but I does not surprise me that it is an unsolved problem.

Humans hallucinate, in the LLM sense, all the time. Did that sign really say that? Nope, I just extrapolated from the first three letters. In the Cambrian Explosion article on HN this morning, I thought the first line said that the earth was desolate. The second line didn't match up with that idea, so I read it again, and the first line said the opposite of what I thought. I particularly hallucinate things into emails from people at work that I disagree with, so much so that I've learned to wait until the next day to reply, and usually I find that they didn't say what I thought they said.