This. For a model to consistently output that Taylor Swift is a comedian or something similarly wrong at reasonable temperature settings, there must be a proble

This. For a model to consistently output that Taylor Swift is a comedian or something similarly wrong at reasonable temperature settings, there must be a problem in the training data. That doesn't mean that "Taylor Swift is a comedian" needs to be in the training data, it it can simply mean that "Taylor Swift" doesn't appear at all. Then "singer" and "comedian" (and tons of other options) will likely appear at similar probabilities during generation. Blaming human semantics for LLMs is generally a bad idea, since we only use human semantics to qualitatively explain how the models abstract ideas. In practice you simply don't know how the model relates words.