An LLM cannot do a lot of things out of the box, and OpenAI has more than a few processing layers already. They certainly already modify output based on verboten topics. Personally I feel any decent LLM should be able to attribute its sources even when not a direct quote, because that raises the bar that much higher on its trustworthiness and general value.

> LLM can’t distinguish out of the box what needs to be cited or not

LLM don’t know that they are supposed to answer questions out of the box either. This is what reinforcement learning from human feedback is good for.

> you need another processing layer

Certainly. For reliability and peace of mind I would implement a traditionally coded plagiarism search on the output. (Traditionally coded as in no AI magic required in that layer) If there is a match you evaluate how significant it is, that most likely is best done with machine learning. For example if the match is a short and simple statement you can ignore it. If it is a longer match, or a very specific statement you need to do something. What you do depends on your resources and risk appetite. Simply supressing the output is an option, it is cheap and very reliable. Rephrasing until it no longer matches is an other, takes a bit more compute and you can end up skirting too close to plagiarism. Rewriting the output to only quote a short amount and provide citation is even more costly, but can be done too.

You can do all of these post processing steps at generation time. And you can also use it to enhance your training dataset. That way the next version of the model will more likely to do the right thing right away, because it learned the correct patterns to quote things.