I don't know what you mean by hallucination here; are you saying that any statistical output is "hallucination"? If so, then we are also constantly hallucinating I guess.

There doesn't seem to be a particularly consistent definition of what "hallucinate" means in the context of LLMs, so let's make one that is in line with the post.

"Hallucination" is when a language model outputs a sequence of tokens comprising a statement (an assertion that is either true or false) that is incorrect. Under this definition, hallucination is clearly not all that an LLM can do.

An easy way to avoid hallucination under this definition is to respond with something that is never a statement when there is a possibility that it can be incorrect; e.g. "I think that... I don't know...". To me, this seems to be what the authors argue. This has always seemed pretty obvious to most people I've spoken to (hell, I've reviewed grant applications from years ago which talk about this), so I'm not sure why it took so long for the "frontier" developers to actually try this.