I recently watched a discussion with a neuroscientist who was explaining the left brain-right brain difference, as it's currently understood. He said the left hemisphere evolved to focus its attention on a small field of perception, mainly to find food. The right hemisphere evolved to detect threats from the entire field of perception.
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.
> The neuroscientist claimed schizophrenia is a disorder of too little inhibition of left brain inferences by the right brain.
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.
Heavyset_go wrote a good answer from one point of view, I would like to add another. Namely that "hallucination" the word means quite different things for a human and an llm. For a human a hallucination roughly refers to a sensory experience where you experience something that isn't there.
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.
Outside of the title hallucination is only used ONCE in the whole article. Further still it is now a widely accepted term of art used to describe a specific behavior exhibited by an LLM that is separate from the biological one.
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.
Well I think there's a contradiction of wants in having AI that's supposed to be the cutting edge of emulating us yet insisting that it shouldn't be anthropomorphized no matter how close it gets to emulating us.
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?
Not sure as I’m not yet familiar enough with the types of errors occurring. I do think a somewhat more accurate and possibly technical set of terms would be more useful for discussions about it though than hallucination.
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?
Exactly right. These are statistical models that operate at a completely different level of abstraction from the human brain: tokens/words/etc for text models, and individual (or perhaps small groups) of pixels for image models. Plausible output is obtained through sheer brute-force, and there is no mechanism to correct what we perceive as errors.
Yes I agree now. "High loss example" while not catchy might be closer. Although it is a bit backwards as you only think about creating the target once you sense the answer is not great.
