Overfitting makes for more human-like output (because it's repeating words written by a human). Out of all possible failure states of a model, overfitting is probably what you want out of an LLM, as long as it's not overfitted enough to lose lawsuits.
I disagree. I'd include overfitting for LLMs as creating unreasonably strong connections to individual sequences used for training, whereas a good mix of that and connections between chunks of those sequences are required.
