Comment by byteknight
17 hours ago
Never thought about it from that perspective, but I think you're right. It is by design, not deceptive intent, just the infinite monkeys theorem where we've replaced randomness with pattern matching trained on massive datasets.
Another way to look at it is everything a LLM creates is a 'hallucination', some of these 'hallucinations' are more useful than others.
I do agree with the parent post. Calling them hallucinations is not an accurate way of describing what is happening and using such terms to personify these machines is a mistake.
This isn't to say the outputs aren't useful, we see that they can be very useful...when used well.
The way I've been putting it for a while is, "all they do is hallucinate—it's the only thing they do. Sometimes the hallucinations are useful."
The OpenAI paper on hallucinations gives actual technical reasons for them, if you're interested.
https://openai.com/index/why-language-models-hallucinate/
https://arxiv.org/abs/2509.04664
The key idea is the model doesn't have any signal on "factual information." It has a huge corpus of training data and the assumption humans generally don't lie to each other when creating such a corpus.
... but (a) we do, and (b) there's all kinds of dimensions of factuality not encoded in the training data that can only be unreliably inferred (in the sense that there is no reason to believe the algorithm has encoded a way to synthesize true output from the input at all).