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277 points simianwords | 1 comments | | HN request time: 0s | source
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rhubarbtree ◴[] No.45152883[source]
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.

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ComplexSystems ◴[] No.45152948[source]
> Trying to eliminate cases where a stochastic model the size of an LLM gives “undesirable” or “untrue” responses seems rather odd.

Why? It seems no less odd than eliminating cases where it gives "undesirable" code snippets with hallucinated errors. This is very important and not odd at all.

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1. rhubarbtree ◴[] No.45153060[source]
To clarify, because you will be left with a biased language model. It will continue to hallucinate, and as you squeeze some hallucinations in one part of the language space you may well create new ones elsewhere. It doesn’t seem a solid line of attack