Naming is a perpetual problem. My issue is with "hallucination", which everyone takes to be the "problem" with GPT style networks making things up. Never mind that transformers are just trying to predict the next likely token, NOT the truth PLUS that they're trained from the internet. As everyone knows, the internet is not known for correctness and truth. If you want any neural network to figure out the truth independently, it'll obviously need the ability to go out into the real world and even needs to be allowed to experiment for most things.

Hallucination used to mean the following. A basic neural network is:

f(x) = y = repeat(nonlinearity(ax[0] + bx[1] + ...))

And then you adjust a, b, c, ... until y is reasonable, according to the cost function. But look! The very same backpropagation can adjust x[0], x[1] ... with the same cost function and only a small change in the code.

This allows you to reverse the question neural networks answer. Which can be an incredibly powerful way to answer questions.

And that used to be called hallucination in Neural networks. Instead of "change these network weights to transform x into y, keeping x constant" you ask "change x to transform x into y, keeping the network weights constant".

Now it's impossible finding half the papers on the this topic. AARGH!