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this post was submitted on 27 Feb 2024
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Technology
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For those of you who didn't read the paper, the argument they're making is similar to Godel's Incompleteness Theorem: no matter how you build your LLM, there will be a significant number of prompts that make that LLM hallucinate. If the proof holds up then hallucinations aren't a limitation of the training data or the structure of your particular model, they're a limitation of the very concept of an LLM. That doesn't make LLMs useless, but it does mean you shouldn't ever use one as a source of truth.
Just want to point out that you shouldn't use them as a single point of truth. They can and do give factual information, but just as with every source, you should verify and know what you're actually doing with the output. Part of why it's so useful to me as a programmer is that I can determine between good and bad outputs and utilize the good ones.
Which is exactly what the paper recommends! As long as you have something that isn't an LLM in the pipeline to vet the output and you're aware is the tech's limitations, they can be useful tools. But some of those limitations might be a more solid barrier than some sales departments would like us to believe.
Yep! Figured id share for the 90% who won't read it though haha. Sales teams truly are the worst when it comes to that
More like the conclusion. I will be shocked if I read this and they're doing Peano arithmetic.