> o3 is also a significant hallucinator. I spent quite a bit of time with it last weekend and found it to be probably far worse than any of the other top models. The catch is that it its hallucinations are quite sophisticated. Unless you are using it on material for which you are extremely knowledgeable, you won't know.

At least 3/4 of humans identify with a religion which at best can be considered a confabulation or hallucination in the rigorous terms you're using to judge LLMs. Dogma is almost identical to the doubling-down on hallucinations that LLMs produce.

I think what this shows about intelligence in general is that without grounding in physical reality it tends to hallucinate from some statistical model of reality and confabulate further ungrounded statements without strong and active efforts to ground each statement in reality. LLMs have the disadvantage of having no real-time grounding in most instantiations; Gato and related robotics projects exempted. This is not so much a problem with transformers as it is with the lack of feedback tokens in most LLMs. Pretraining on ground truth texts can give an excellent prior probability of next tokens and I think feedback either in the weights (continuous fine-tuning) or real-world feedback as tokens in response to outputs can get transformers to hallucinate less in the long run (e.g. after responding to feedback when OOD)