Who does fundamentally understand how LLMs work? Many claims flying around these days, all backed by some of the largest investments ever collectively made by humans. Lots of money to be lost because of fundamental misunderstandings.
Personally, I find that AI influencers conveniently brush away any evidence (like inability to perform basic arithmetic) about how LLMs fundamentally work as something that should be ignored in favor of results like TFA.
Do LLMs have utility? Undoubtedly. But it’s a giant red flag for me that their fundamental limitations, of which there are many, are verboten to be spoken about.
I feel like I'm saying "this hammer is so cool, it's made driving nails a breeze" and people go "but it can't screw screws in! Why won't anyone talk about that! Hammers really aren't all they're cracked up to be".
My comment even said “LLMs have utility”. I gave an inch, and now the mile must be taken.
Talking about how they find it hard to say they aren't sure of something is a much more interesting limitation to talk about, for example.
Sure, thank you for steelmanning my argument. I didn’t think I needed to actually spell out all of the fundamental limitations of LLMs in this specific thread. They are spoken at length across the web, but are often met with pushback, which was my entire point.
Here’s another one: LLMs do not have a memory property. Shut off the power and turn it back on and you lose all context. Any “memory” feature implemented by companies that sell LLM wrappers are a hack on top of how LLMs work, like seeding a context window before letting the user interact with the LLM.
Given the precariousness of managing LLM context windows, I don’t think it’s particularly unfair to assume that LLMs that learn without limit become very unstable.
To steelman, if it’s possible, it may be prohibitively expensive. But somehow I doubt it’s possible.
LOL, an AI wrote this