Why language models hallucinate

I find this rather oddly phrased.

LLMs hallucinate because they are language models. They are stochastic models of language. They model language, not truth.

If the “truthy” responses are common in their training set for a given prompt, you might be more likely to get something useful as output. Feels like we fell into that idea and said - ok this is useful as an information retrieval tool. And now we use RL to reinforce that useful behaviour. But still, it’s a (biased) language model.

I don’t think that’s how humans work. There’s more to it. We need a model of language, but it’s not sufficient to explain our mental mechanisms. We have other ways of thinking than generating language fragments.

Trying to eliminate cases where a stochastic model the size of an LLM gives “undesirable” or “untrue” responses seems rather odd.

People also tend not to understand the absurdity of assuming that we can make LLMs stop hallucinating. It would imply not only that truth is absolutely objective, but that it exists on some smooth manifold which language can be mapped to.

That means there would be some high dimensional surface representing "all true things". Any fact could be trivially resolved as "true" or "false" simply by exploring whether or not it was represented on this surface. Where or not "My social security number is 123-45-6789" is true could be determined simply by checking whether or not that statement was mappable to the truth manifold. Likewise you could wander around that truth manifold and start generating output of all true things.

If such a thing existed it would make even the wildest fantasies about AGI seem tame.

edit: To simplify it further, this would imply you could have an 'is_true(statement: string): bool' function for any arbitrary statement in English.

Well, no. The article pretty much says that any arbitrary statement can be mapped to {true, false, I don't know}. This is still not 100% accurate, but at least something that seems reachable. The model should just be able to tell unknowns, not be able to verify every single fact.

Determining a statement's truth (or if it's outside the system's knowledge) is an old problem in machine intelligence, with whole subfields like knowledge graphs and such, and it's NOT a problem LLMs were originally meant to address at all.

LLMs are text generators that are very good at writing a book report based on a prompt and the patterns learned from the training corpus, but it's an entirely separate problem to go through that book report statement by statement and determine if each one is true/false/unknown. And that problem is one that the AI field has already spent 60 years on, so there's a lot of hubris in assuming you can just solve that and bolt it onto the side of GPT-5 by next quarter.

> And that problem is one that the AI field has already spent 60 years on

I hope you don't think that the solutions will be a closed-form expression. The solution should involve exploration and learning. The things that LLMs are instrumental in, you know.