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Why language models hallucinate

(openai.com)
277 points simianwords | 1 comments | 06 Sep 25 07:41 UTC | HN request time: 0.196s | source
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amelius ◴[06 Sep 25 13:39 UTC] No.45149170[source]▶
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.

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1. codethief ◴[07 Sep 25 08:33 UTC] No.45156457[source]▶
I was inclined to agree at first but do those use cases really conflict?

If I ask the LLM to generate a fictional story set in medieval Francs, and it then responds with a fictional story set in medieval France, that's an appropriate ("correct") response to the task I gave it. If it responded with a story set in medieval England, though, that would not be correct. If, instead, I had asked it to generate a story in "medieval times", both France and England would have been correct as locations because the problem was underspecified and asked for some creativity. A medieval story set in the US, however, would still not have been correct or consistent with the training data. You can come up with more such examples even in entirely fictional settings: Once the story has been set to take place in fictional city X, it would not be consistent if two sentences later the characters were in city Y all of a sudden. (That would be a bit too creative.) What I'm trying to say is: Creativity might be "correct" (appropriate) in a given context, or it might not be. Even fiction and creativity require a certain degree of consistency and coherence.

Now, correct answers, in turn, might also require a certain degree of creativity:

If I ask the LLM for some straight up facts, which are not in its training data nor in the prompt context, the only really correct answer is "I don't know". However, sometimes it might be possible to narrow down the correct answer to a few possible options based on the training data. So then it might be appropriate for the LLM to say "I don't know the exact answer but here are some educated guesses based on what I do know: …" And maybe, having pondered those options, it is able to deduce the correct answer after all. (In the same way as I am writing this HN comment to help me think and clarify my thoughts.)

This is reminiscent of mathematics and mathematical research, which are often described as a creative process. Obviously, the creative output is heavily constrained. You make educated guesses and then validate them against what you already know to be true. Someone else here in this thread[0] mentioned Popper's "Conjectures and Refutations" as a possible model for what intelligent cognition is about and the more I think about that, the more convincing I find it.

[0]: https://news.ycombinator.com/item?id=45153695