Not the same thing, LLMs are stochastic parrots, and part of that is by design, so that they produce output that emulates speech. But hallucinations are not av

Not the same thing, LLMs are stochastic parrots, and part of that is by design, so that they produce output that emulates speech. But hallucinations are not avoidable either. https://arxiv.org/abs/2401.11817 https://arxiv.org/abs/2401.11817 They are Internet simulators unless they can find some small, polynomial, matches to their existing patterns. The fact that they will spit out confident incorrect answers mixed with automation bias on the humans part is challenging. But while answering 'yes' in Propositional logic is cheap, answering 'no' is exponential time. Once you hit first order logic you start to hit finite time and uncomputability. LLMs work well with learnable problems and searching for approximate results in complex high dimensional spaces. But we also don't know the conversation context in this case which may have lead to this response if it wasn't just a stochastic match. IMHO the unreliable responses are a feature, because humans suffer from automation bias, and one of the best ways to combat it is for the user to know that the system makes mistakes. If you are in a domain where it's answers are fairly reliable the results tend to be accepted despite the knowledge an individual has.