It's a function of the LLM "thought process"! It's not really modeled after human speech. It is in short segments but not long form, same reason you see the same rather odd nuances in LLM generated code.

If they way you thought was to run a bunch of if statements, generate content, then feed that content back to get a "score" of what seems the most plausible, run the if statements again, and adjust / merge responses, then you would write similarly. The recognizable cadence of LLM generated content is pretty clearly the result of a lot of if statements being fused together.