"ChatGPT’s intercoder agreement exceeds that of both crowd-workers and trained annotators for all tasks." -- wait, what? However good ChatGPT is at approximati

"ChatGPT’s intercoder agreement exceeds that of both crowd-workers and trained annotators for all tasks." -- wait, what? However good ChatGPT is at approximating trained annotators for these Twitter tasks, it's an algorithm, so the level of simulated "inter-annotator agreement" is in the authors' control (in the case of GPT, via the temperature parameter, for which they try just two values, 0.2 and 1.) And why does this paper not make any effort to describe the wide range of annotation tasks for which this kind of simulated annotation is not a good idea -- for example, where you care about the subjective opinions of specific subgroups of people at specific times. And even for the tasks they mention, what about the risks of reinforcing biases by using a model's output to train new models? Good grief this paper is lazy!