Maybe the solution isn't to mitigate hallucinations, but to make them more interesting. In an single LLM response hallucinations are misleading, but in a genera

Maybe the solution isn't to mitigate hallucinations, but to make them more interesting. In an single LLM response hallucinations are misleading, but in a generative adversarial network of LLMs they could be intuitive leaps. I'd wonder whether there's limited value in effort to optimize against them. When human language models hallucinate, we attribute it to things like jealousy, conceit, anger, and negative emotions. Not saying LLMs have them, but in an agggregated group of them, evolving a characterization of each others hallucinations could be analogous.