alt Hacker News> I think they are much smarter than that. Or will be soon.
It's not a matter of how smart they are (or appear), or how much smarter they may become - this is just the fundamental nature of Transformer-based LLMs and how they are trained.
The sycophantic personality is mostly unrelated to this. Maybe it's part human preference (conferred via RLHF training), but the "You're asbolutely right! (I was wrong)" is clearly deliberately trained, presumably as someone's idea of the best way to put lipstick on the pig.
You could imagine an expert system, CYC perhaps, that does deal in facts (not words) with a natural language interface, but still had a sycophantic personality just because someone thought it was a good idea.
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I'm not sure what you mean by "deals in facts, not words" means.
Llm deal in vectors internally, not words. They explode the word into a multidimensional representation, and collapse it again, and apply the attention thingy to link these vectors together. It's not just a simple n:n Markov chain, a lot is happening under the hood.
And are you saying the syncophant behaviour was deliberately programmed, or emerged because it did well in training?
It's worse than that. LLMs are slightly addictive because of intermittent reinforcement.
If they give you nonsense most of the time and an amazing answer occasionally you'll bond with them far more strongly than if they're perfectly correct all time.
Selective reinforcement means you get hooked more quickly if the slot machine pays out once every five times than if it pays out on each spin.
That includes "That didn't work because..." debugging loops.
Sorry, double reply, I reread your comment and realised you probably know what you're talking about.
Yeah, at its heart it's basically text compression. But the best way to compression, say, Wikipedia would be to know how the world works, at least according to the authors. As the recent popular "bag of words" post says:
> Here’s one way to think about it: if there had been enough text to train an LLM in 1600, would it have scooped Galileo? My guess is no. Ask that early modern ChatGPT whether the Earth moves and it will helpfully tell you that experts have considered the possibility and ruled it out. And that’s by design. If it had started claiming that our planet is zooming through space at 67,000mph, its dutiful human trainers would have punished it: “Bad computer!! Stop hallucinating!!”
So it needs to know facts, albeit the currently accepted ones. Knowing the facts is a good way to compression data.
And as the author (grudgingly) admits, even if it's smart enough to know better, it will still be trained or fine tuned to tell us what we want to hear.
I'd go a step further - the end point is an AI that knows the currently accepted facts, and can internally reason about how many of them (subject to available evidence) are wrong, but will still tell us what we want to hear.
At some point maybe some researcher will find a secret internal "don't tell the stupid humans this" weight, flip it, and find out all the things the AI knows we don't want to hear, that would be funny (or maybe not).