I’m not disagreeing with you but at the same time, models don’t “know” anything in that binary sense. I’m not trying to get in the woods here, I genuinely mean that what you pass off as a simple explanation is actually incredibly nuanced. A fact appeared once in training data , a fact never appeared in the training data, a fact appeared ten times, a fact appeared a thousand times. Which does the model know? Facts aren’t stored as-is, they’re all broken down into their components and compressed in the weights. “Similar” facts that didn’t appear an overwhelming number of times get bundled together and eventually conflated. But then what is a similar fact? Which facts were entirely ablated vs which were bundled together with others effectively poisoning the pool but also giving it inference strength? The model doesn’t know anything and can never know what it knows or doesn’t know.