[seanhunter]

Yes exactly. It seems intuitive that the model could generate a better distribution and thus cure hallucination but that doesn't actually match what the model does.

The model doesn't sample a probability distribution of individual "facts"[1] it samples a probability distribution of tokens which are generally parts of words, bits of punctuation etc. That we get "facts" out of it which may even be wrong in the first place is an emergent behaviour because of the attention mechanism.

Totally agree that it's a deeper problem and may be intrinsic to the design of the models and the fact that they are trained on a next word prediction task. Karpathy talks about the models as "dreaming text". In that sense it's not surprising that some of it is whacky. Our dreams are too.

[1] By which I mean atomic things that can be right or wrong

[radarsat1]

Agreed. I have a loose idea that hallucination is related to training to maximize the probability of individual tokens while ignoring the joint sequence probability, which is along the lines of what you are saying -- it is not trained to output the most probable final sequence, so it gets stuck in the "wrong place" half way through.