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.

But an LLM is not answering "what is truth?". It's "answering" "what does an answer to the question "what is truth?" look like?".

It doesn't need a conceptual understanding of truth - yes, there are far more wrong responses than right ones, but the right ones appear more often in the training data and so the probabilities assigned to the tokens which would make up a "right" one are higher, and thus returned more often.

You're anthropomorphizing in using terms like "lying to us" or "know the truth". Yes, it's theoretically possible I suppose that they've secretly obtained some form of emergent consciousness and also decided to hide that fact, but there's no evidence that makes that seem probable - to start from that premise would be very questionable scientifically.

A lot of people seem to be saying we don't understand what it's doing, but I haven't seen any credible proof that we don't. It looks miraculous to the relatively untrained eye - many things do, but just because I might not understand how something works, it doesn't mean nobody does.

>But an LLM is not answering "what is truth?". It's "answering" "what does an answer to the question "what is truth?" look like?".

You don't actually know this right? You said what I'm saying is theoretically possible so you're contradicting what you're saying.

>You're anthropomorphizing in using terms like "lying to us" or "know the truth". Yes, it's theoretically possible I suppose that they've secretly obtained some form of emergent consciousness and also decided to hide that fact, but there's no evidence that makes that seem probable - to start from that premise would be very questionable scientifically.

Where did I say it's conscious? You hallucinated here thinking I said something I didn't.

Just because you can lie doesn't mean you're conscious. For example, a sign can lie to you. If the speed limit is 60 but there's a sign that says the speed limit is 100 then the sign is lying. Is the sign conscious? No.

Knowing is a different story though. But think about this carefully. How would we determine whether a "human" knows anything? We only can tell whether a "human" "knows" things based on what it Tells us. Just like an LLM. So based off of what the LLM tells us, it's MORE probable that the LLM "knows" because that's the SAME exact reasoning on how we can tell a human "knows". There's no other way we can determine whether or not an LLM or a human "knows" anything.

So really I'm not anthropomorphizing anything. You're the one that's falling for that trap. Knowing and lying are not unique concepts to conciousness or humanity. These are neutral concepts that exist beyond what it means to be human. When I say something, "knows" or something "lies" I'm saying it from a highly unbiased and netural perspective. It is your bias that causes you to anthropomorphize these concepts with the hallucination that these are human centric concepts.

>A lot of people seem to be saying we don't understand what it's doing, but I haven't seen any credible proof that we don't.

Bro. You're out of touch.

https://www.youtube.com/watch?v=qrvK_KuIeJk&t=284s

Hinton, the godfather of modern AI says we don't understand. It's not people saying we don't understand. It's the generally understanding within academia is: we don't understand LLMs. So you're wrong. You don't know what you're talking about and you're highly misinformed.

I think your assessment of the academic take on AI is wrong. We have a rather thorough understanding of the how/why of the mechanisms of LLMs, even if after training their results sometimes surprise us.

Additionally, there is a very large body of academic research that digs into how LLMs seem to understand concepts and truths and, sure enough, examples of us making point edits to models to change the “facts” that they “know”. My favorite of that corpus, though far from the only or most current/advances research , is the Bau Lab’s work: https://rome.baulab.info/