Possibly, but I struggle to reason about Bayesian nets at that scale. I think the level at which a Bayesian net could “know what it doesn’t know” would be regar

Possibly, but I struggle to reason about Bayesian nets at that scale. I think the level at which a Bayesian net could “know what it doesn’t know” would be regarding uncertainty in what text to generate in a given context, not whether or not the generated text is saying something true. One example could be a prompt in a language not seen in the training data. It could be that some plausible sounding made up thing is likely in a given context. Also, at the end of the day, what you’ll get out of a Bayesian LLM is a sample of several generated texts which would hopefully have more variation than multiple samples from the same standard LLM. I can see it being helpful to see if the different outputs agree or not, but I can’t tell at a glance how well it would work in practice.