Not necessarily. While this may happen sometimes, fundamentally hallucinations don't stem from there being errors in the training data (with the implication tha

Not necessarily. While this may happen sometimes, fundamentally hallucinations don't stem from there being errors in the training data (with the implication that there would be no hallucinations from models trained on error-free data). Hallucinations are inherent to any "given N tokens, append a high-probability token N+1"-style model. It's more complicated than what happens with Markov chain models but you can use them to build an intuition for what's happening. Imagine a very simple Markov model trained on these completely factual sentences: - "The sky is blue and clear" - "The ocean is blue and deep" - "Roses are red and fragrant" When the model is asked to generate text starting with "The roses are...", it might produce: "The roses are blue and deep" This happens not because any training sentence contained incorrect information, but because the model learned statistical patterns from the text, as opposed to developing a world model based on physical environmental references.