The goal (literal, not figurative) of an LLM is to reduce training/inference errors - to predict the training material.
The "quality/goodness" (likability) of output is only selected for to extent that it also reflects RLHF preferences originating from low paid contractors who are scoring based on "looks good to me", not based on Pulitzer prize prose potential.
To explore a false hypothetical, suppose an LLM was only trained on the output of a single popular author, then you'd certainly expect it to reproduce that author's style, but it would not be "upredictably interesting" but rather predictably quirky, reproducing the same "surprises" over and over so that they no longer are surprising and therefore interesting.
To continue the hypothetical, if you trained an LLM exclusively on the output of multiple good authors, then rather than get the best of each you'd get a statistical blend (this is how training works) that emphasized what they had in common rather than the individual quirks that made them interesting.
There is a fairly common misperception that "AI" (LLMs) is always trying to do it's best, where "best" has been anthropomorphized to have the human sense rather than the LLM sense of "the most statistically accurate continuation prediction" it can make. For example, if you give an LLM the moves of a partial chess game and ask it to predict next moves for one of the players, it will NOT play to win, but will rather play to predict how the game would have continued, which means continuing at the level (ELO rating) and style of the player who had made those moves.
Of course you can ask an LLM to play to win, just as you can ask it to write in a style that is, say, "unpredictable and interesting", but it can't escape itself - you will necessarily get predictable unpredictability.