Rant, I think the usage of the term hallucination in this context is entirely too anthropomorphic.
The way we train LLMs, to my understanding, is to predict the next word. That's a narrow task, and in my interpretation is a left brain dominant task.
What can happen with the left brain is that it can become so focused on the task at hand, and so wedded to the mental models used to accomplish that task, that it loses touch with reality as broadly perceived by the right brain. The neuroscientist claimed schizophrenia is a disorder of too little inhibition of left brain inferences by the right brain. (I am taking him at his word on this.) The left brain's mental models lose touch with ground truth. This can result in hallucinations.
If I am correct that LLMs act like the left brain in that they are relying on models that they developed during training and focusing on a single task, absent sensory experience, then hallucination may actually be a good term.
I do think that the use of anthropomorphic terms is problematic because it suggests the same phenomenon, rather than an analogous phenomenon.
This sounds very "bicameral mind"-y[1] to me, and it's worth noting that schizophrenia's causes haven't been pinned down accurately yet. There are a ton of hypotheses around how schizophrenia develops/works, but none are conclusive.
I bring it up because when you hear relatively simple explanations about the left and right brain and the tasks they're "assigned" or "designed" to process, or how their perceived differences contribute to mental illness, it should be taken with a grain of salt.
Similarly, I'd hesitate to compare LLMs or any NNs to actual human brains. Their similarities are entirely superficial, and beyond the very basic topology of NNs that were inspired by specific types of neurons, the similarities end there.
[1] https://en.wikipedia.org/wiki/Bicameral_mentality#The_Origin...
For an LLM it refers to making something up when answering a question. A closer phenomenon to that would be a false memory, but even that isn't quite the same thing.
The thing is that we want LLM's to learn patterns, we want them to generalize from its training data to a certain extent. Hallucinations are basically undesirable instances of this. The user expects to hear about somethign that does exist, but the answer they get is rather about somethign that the LLM has extrapolated might exist.
It seems anytime someone brings up these models being "anthropomorphized" they realize they have nothing relevant to say but feel the needs to say something and are falling back on a tired platitude.
These are token prediction models. Their token predictions are accurate, insofar as the probabilities of those tokens in those sequences being representative of the kind of thing they were trained on.
Is it plausible that a document exists that explains that George Washington was actually an amateur magician? How is a GPT model to know that that is not the document it is predicting the next tokens for? Why wouldn't it explain that he used to perform for children's parties, and do tricks involving making doves and rabbits appear?
Or "high-loss example"
When we give information we don't know to be true -which is what the model does when it builds low confidence tokens on each other- we call it guessing. Why would this be any different?
Hallucination, on the other hand, is evocative of a less reasonable/rational source.