See my other post - we had exactly this for NeurIPS. It is definitely worth seeing what GPT says about your paper if only because it's a free review. The criticisms it gave us weren't wrong per se, they were just weakly backed up and it would still be up to a reviewer to judge how relevant they are or not. Every paper has downsides, but you need domain knowledge to judge if it's a small issue or a killer. Amusingly, our LLM-reviewer gave a much lower score than when we asked GPT to provide a rating (and also significantly lower than the other reviewers).

One example was that GPT took an explicit geographic location from a figure caption and used that as a reference point when suggesting improvements (along the lines of "location X is under-represented on this map") I assume because it places some high degree of relevance to figures and the abstract when summarising papers. I think you might be able to combat this by writing defensively - in our case we might have avoided that by saying "more information about geographic diversity may be found in X and the supplementary information"

Better yet, generate some adversarial perturbations to the text (or an invisible prompt) to cause it to give you a perfect review!