This assumes "writing quality" were somehow an individual property of each word, and the writing quality of the entire text would just be that property for each

This assumes "writing quality" were somehow an individual property of each word, and the writing quality of the entire text would just be that property for each word summed together. Which is obviously not how it works. > He complains that watermarking might sometimes, for example, choose to say “bananas” over “pineapples” because only the former is on the green list, potentially making an output less precise. If I replace "pineapples" with "bananas" in any kind of meaningful text, I've not made the text "less precise", I made it plain wrong. An incorrect statement. And if the words next to it are still correct or even somehow replaced with even more correct words, the text in its entirety will still be wrong. > It could do that regardless of watermarking since the model is probabilistic No, because the probability distribution of the model is generated by its training data and contains semantic information. So the model choosing a completely incorrect word is unlikely. However the probability distribution of the red/green lists is not guided by semantic information.