Comment by stratos123

I wouldn't draw such general conclusions out of this, because it doesn't explain why LLM outputs are samish. Why shouldn't a generic prompt produce a mix of generic-but-notably-different outputs, as would be the case if you sampled humans? AFAIK, the answer is that it's RLHF that causes models to collapse to a specific style, because base models tend to score much better on creativity and output diversity evaluations than post-trained ones.