Wanting to use accurate language isn't exhausting, it's a requirement if you want to think about and discuss problems clearly.
I don't think that's the case here: there is a very real difference between describing something with a model that implies one (false) thing vs. a model that doesn't have that flaw.
If you don't find that convincing, then consider this: by taking the time to properly define things at the beginning, you'll save yourself a ton of time later on down the line – as you don't need to untangle the mess that resulted from being sloppy with definitions at the start.
This is all a long way of saying that aiming to clarify your thoughts is not the same as arguing pointlessly over definitions.
Words can mean more than one thing. And sometimes the new meaning is significantly different but once everyone accepts it, there's no confusion.
You're arguing that we shouldn't accept the new meaning - not that "it doesn't mean that" (because that's not how language works).
I think it's fine - we'll get used to it and it's close enough as a metaphor to work.
It feels like you're assuming that we're already 60 years past re-defining "hallucination" and the consensus is established, but the fact that people are quibbling about it right now is a sign that the definition is currently in transition/ has not reached consensus.
What value is there in trying to shut down the consensus-seeking discussion that gave us "computer"? The same logic could be used arguing that "computers" are actually be called "calculators" and why are people still trying to call it a "computer"?
> Here we develop new methods grounded in statistics, proposing entropy-based uncertainty estimators for LLMs to detect a subset of hallucinations—confabulations—which are arbitrary and incorrect generations.