Most people don’t understand the technology and maths at play in these systems. That’s normal, as is using familiar words that make that feel less awful. If you have a genuine interest in understanding how and why errant generated content emerges, it will take some study. There isn’t (in my opinion) a quick helpful answer.
I genuinely want to understand whether there’s a meaningful difference between non-hallucinatory and hallucinatory content generation other than “real world correctness”.
I’m far from an expert but as I understand it the reference point isn’t so much the “real world” as it is the training data. If the model generates a strongly weighted association that isn’t in the data, and shouldn’t exist perhaps at all. I’d prefer a word like “superstition”, it seems more relatable.
