Why language models hallucinate

They hallucinate because it's an ill-defined problem with two conflicting usecases:

1. If I tell it the first two lines of a story, I want the LLM to complete the story. This requires hallucination, because it has to make up things. The story has to be original.

2. If I ask it a question, I want it to reply with facts. It should not make up stuff.

LMs were originally designed for (1) because researchers thought that (2) was out of reach. But it turned out that, without any fundamental changes, LMs could do a little bit of (2) and since that discovery things have improved but not to the point that hallucination disappeared or was under control.

Indeed - as Rebecca Parsons puts it, all an LLM knows how to do is hallucinate. Users just tend to find some of these hallucinations useful, and some not.

This is a a super helpful way of putting it. I've tried to explain to my less technical friends and relatives that from the standpoint of an LLM, there's no concept of "truth", and that all it basically just comes up with the shape of what a response should look like and then fills in the blanks with pretty much anything it wants. My success in getting the point across has been mixed, so I'll need to try out this much more concise way of putting it next time!

But this explanation doesn’t fully characterize it does it?

Have the LLM talk about what “truth” is and the nature of LLM hallucinations and it can cook up an explanation that demonstrates it completely understands the concepts.

Additionally when the LLM responds MOST of the answers are true even though quite a bit are wrong. If it had no conceptual understanding of truth than the majority of its answers would be wrong because there are overwhelmingly far more wrong responses than there are true responses. Even a “close” hallucination has a low probability of occurring due to its proximity to a low probability region of truth in the vectorized space.

You’ve been having trouble conveying these ideas to relatives because it’s an inaccurate characterization of phenomena we don’t understand. We do not categorically fully understand what’s going on with LLMs internally and we already have tons of people similar to you making claims like this as if it’s verifiable fact.

Your claim here cannot be verified. We do not know if LLMs know the truth and they are lying to us or if they are in actuality hallucinating.

You want proof about why your statement can’t be verified? Because the article the parent commenter is responding to is saying the exact fucking opposite. OpenAI makes an opposing argument and it can go either way because we don’t have definitive proof about either way. The article is saying that LLMs are “guessing” and that it’s an incentive problem that LLMs are inadvertently incentivized to guess and if you incentivize the LLM to not confidently guess and to be more uncertain the outcomes will change to what we expect.

Right? If it’s just an incentive problem it means the LLM does know the difference between truth and uncertainty and that we can coax this knowledge out of the LLM through incentives.

> Have the LLM talk about what “truth” is and the nature of LLM hallucinations and it can cook up an explanation that demonstrates it completely understands the concepts.

There is not necessarily a connection between what an LLM understands and what it says. It’s totally possible to emit text that is logically consistent without understanding. As a trivial example, just quote from a physics textbook.

I’m not saying your premise is necessarily wrong: that LLMs can understand the difference between truth and falsehood. All I’m saying is you can’t infer that from the simple test of talking to an LLM.