[HarHarVeryFunny]

I don't think there's any narrow definition of what "hallucination" means. It generally refers to the model giving non-factual answers in contexts that are meant to be factual, but not all causes of this are going to be fixable without very major changes.

The fundamental issue is that most of the time LLMs are going to be combining statistics derived from many training samples when generating a single continuation, and there is just no guarantee that this will result in a semantically coherent response. Of course the model's depth of parsing and semantic analysis usually means that each generated word is highly plausible, but this isn't the same as being factually correct, especially so in these cases where the model is drawing on multiple sources to create a mashup response, which is the normal mode of operation.