I think some of it is mitigated relatively straightforwardly in UX by having a relatively dumb, discrete model that appends hardcoded warnings. That way the ul

I think some of it is mitigated relatively straightforwardly in UX by having a relatively dumb, discrete model that appends hardcoded warnings. That way the ultra clever, complex natural language model can go wild responding to your request for a scientific paper about chicken chicken chicken written in pirate-speak, whilst dumb old model that simply scans for indications the interaction has a citation in it appends a hardcoded "it looks like this response contains citations. Please be aware these citations may not refer to real documents" disclaimer without being perturbed by the lack of intersection between citation warnings and pirate speak. And if that's subject to the "Scunthorpe problem" where it flags up loads of false positives who cares. It's a separate model that doesn't impact upon the quality of the actual GPT, it's not as annoying a fail as GPT bias towards refusing answers, and even when its labels are wrong it's aligned with the goal of training the human not to trust computer generated responses so much