As we continue to push the boundaries of Large Language Models (LLMs), I'd like to pose a question that has been at the forefront of my mind:
Can we achieve true creativity in LLMs by decoupling the notion of "training data" from the concept of "domain expertise," or will this approach only perpetuate the phenomenon of "hallucinations" in AI, where the model attempts to fill knowledge gaps with plausible-sounding but ultimately incorrect information?
In other words, can we truly empower LLMs to think outside the box and push the boundaries of human knowledge, or will they forever be bound by the limitations of the data they were trained on?
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