LLM input tends to, above all else, end up *super* verbose, especially when wielded by people that don't actually understand what they are trying to get done.
So while that assessment may stand, the challenge is *volume*. If you see a large LLM contribution, you know it's going to be a slog, and your feedback is likely to be a telephone game between you, the human curator of the work, and the LLM they are actually interacting with, with the human in the middle adding nothing but confusion to the whole thing. If they just stated what they wanted plainly, then the project could have, at their option, used LLM even better than the submittor could have, if it would help.
You have issues like: https://github.com/rhinstaller... Where a detailed rationale is provided, and from a human one would have assumed they wouldn't have had such a concrete analysis unless they had a point, then it turns out that it was LLM hallucinated guesswork with no bearing whatsoever on the reported problem. I've had a few like that where they proposed broken nonsense because the LLM made a credible sounding guess, where their change didn't actually fix the problem, but instead did something pointless or even worse, just swallowed the error message.