Thanks for pointing out the elephant in the room with LLMs.
The basic design is non-deterministic. Trying to extract "facts" or "truth" or "accuracy" is an exercise in futility.
You can't blame an LLM for getting the facts wrong, or hallucinating, when by design they don't even attempt to store facts in the first place. All they store are language statistics, boiling down to "with preceding context X, most statistically likely next words are A, B or C". The LLM wasn't designed to know or care that outputting "B" would represent a lie or hallucination, just that it's a statistically plausible potential next word.
But they are like a smart student trying to get a good grade (that's how they are trained!). They'll agree with us even if they think we're stupid, because that gets them better grades, and grades are all they care about.
Even if they are (or become) smart enough to know better, they don't care about you. They do what they were trained to do. They are becoming like a literal genie that has been told to tell us what we want to hear. And sometimes, we don't need to hear what we want to hear.
"What an insightful price of code! Using that API is the perfect way to efficiently process data. You have really highlighted the key point."
The problem is that chatbots are trained to do what we want, and most of us would rather have a syncophant who tells us we're right.
The real danger with AI isn't that it doesn't get smart, it's that it gets smart enough to find the ultimate weakness in its training function - humanity.
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.
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?
And yes, I know what an expert system is.
Do you know that a neural network (or set of matrices, same thing really) can approximate anything else? https://en.wikipedia.org/wiki/Universal_approximation_theore...
How do you know that inside the black box, they don't approximate expert systems?
The universal approximation theorem is not relevant. You would first have to try to train the neural network to approximate a constraint solver (that's not the case with LLMs), and in practice, these kinds of systems are exactly the ones that a neural network is bad at.
The universal approximation theory says nothing about feasibility, it only talks about theoretical existence as a mathematical object, not whether the object can actually be created in the real world.
I'll remind you that the expert system would have to have been created and updated by humans. It would have had to have been created before a neural network was applied to it in the first place.