Any proposed solution would only serve to make the next iteration of the model better at avoiding detection (e.g. incorporating a GAN-style training cycle).
Just as with Poe's law, there simply comes a point at which it becomes impossible to recognise AI generated texts just by analysing them. Made-up references might be a clue, but that's very context-dependent.
In a style of a very concise HN comment describe how to detect that a text has been written by the Assistant.
To detect if a text has been written by the Assistant, check for the use of generic language, repetitive phrases, and a lack of personal perspective or opinion.
There are many people who use generic language, repetitive phrases, and lack personal perspectives and opinions (at least people who lack sophisticated perspectives/opinions).
Which makes the problem more complicated, but also perhaps irrelevant. If you can’t tell whether some text was written by a human or a computer, does it even matter?
At some point the text will become undetectable. The same happens for image generating Networks. The detection works by detecting a statistical discrepancy between the AI output and "real" data. The goal of the AI in training is also to close that gap, the better the network, the harder it is to detect and the more output data you would need to get any significant results.