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An LLM is a lossy encyclopedia

(simonwillison.net)
509 points tosh | 1 comments | 29 Aug 25 09:40 UTC | HN request time: 0.346s | source

(the referenced HN thread starts at https://news.ycombinator.com/item?id=45060519)
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kgeist ◴[02 Sep 25 11:46 UTC] No.45101806[source]▶
I think an LLM can be used as a kind of lossy encyclopedia, but equating it directly to one isn't entirely accurate. The human mind is also, in a sense, a lossy encyclopedia.

I prefer to think of LLMs as lossy predictors. If you think about it, natural "intelligence" itself can be understood as another type of predictor: you build a world model to anticipate what will happen next so you can plan your actions accordingly and survive.

In the real world, with countless fuzzy factors, no predictor can ever be perfectly lossless. The only real difference, for me, is that LLMs are lossier predictors than human minds (for now). That's all there is to it.

Whatever analogy you use, it comes down to the realization that there's always some lossiness involved, whether you frame it as an encyclopedia or not.

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jbstack ◴[02 Sep 25 12:08 UTC] No.45102030[source]▶
Are LLMs really lossier than humans? I think it depends on the context. Given any particular example, LLMs might hallucinate more and a human might do a better job at accuracy. But overall LLMs will remember far more things than a human. Ask a human to reproduce what they read in a book last year and there's a good chance you'll get either absolutely nothing or just a vague idea of what the book was about - in this context they can be up to 100% lossy. The difference here is that human memory decays over time while a LLM's memory is hardwired.
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1. ijk ◴[02 Sep 25 12:58 UTC] No.45102543[source]▶
I think what trips people up is that LLMs and humans are both lossy, but in different ways.

The intuitions that we've developed around previous interactions are very misleading when applied to LLMs. When interacting with a human, we're used to being able to ask a question about topic X in context Y and assume that if you can answer it we can rely on you to be able to talk about it in the very similar context Z.

But LLMs are bad at commutative facts; A=B and B=A can have different performance characteristics. Just because it can answer A=B does not mean it is good at answering B=A; you have to test them separately.

I've seen researchers who should really know better screw this up, rendering their methodology useless for the claim they're trying to validate. Our intuition for how humans do things can be very misleading when working with LLMs.