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.
eldrichhydralisk
joined 1 year ago
Mastodon is very active after you start following enough people and hashtags to populate your feed. It's a bit rough to get started though: no algorithm means no content (or very random content in the local/federated feeds) until you build it up for yourself. But once you hit critical mass, I've found it a much nicer experience than I ever got on Twitter.
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.