It's not a bold assumption. t's the only assumption. We can't control the output of llms completely because we don't know how they generate. Nobody on earth has the faintest clue how all those 175 billion paramters are shaping the response to input.

It doesn't matter bout "thinking" or whatever. Any black box system will be uncontrollable in essence. You can not make inviolable rules for a system you don't understand.

And saying LLMs hallucinate because they don't understand anything is stupid. And just shows ignorance on your part. Models hallucinate because they're rewarded for plausibly guessing during training when knowledge fails. Plausibly guessing is a much better strategy to reducing loss.

And the conclusion is obvious enough. Bugger smarter models hallucinate less because they guess less. That holds true.

https://crfm.stanford.edu/helm/latest/?group=core_scenarios

All the instruct tuned models on this list follow that trend.

From Ada to Babbage to Curie to Claude to Davinci-002/003. Greater size equals Greater truthfulness (evaluated on TruthfulQA)