There's a way to anthropomorphize large language models that I think is less misleading: they are like a well-read actor that always "wants" to play "let's pret

There's a way to anthropomorphize large language models that I think is less misleading: they are like a well-read actor that always "wants" to play "let's pretend." LLM's are trained on "fill in the blank" which means they follow the "yes, and" rule of improv. They are very willing to follow your lead and to assume whatever role is necessary to play their part. If you give them hints about what role you want by asking leading questions, they will try to play along and pretend to hold whatever opinions you might want from them. What are useful applications for this sort of actor? It makes sense that language translation works well because it's pretending to be you, if you could speak a different language. Asking them to pretend to be a Wikipedia article without giving them the text to imitate is going to be hit and miss since they're just as willing to pretend to be a fake Wikipedia article, as they don't know the difference. Testing an LLM to find out what it believes is unlikely to do anything useful. It's going to pretend to believe whatever is consistent with the role it's currently playing, and that role may be chosen randomly if you don't give it any hints. It can be helpful to use prompt engineering to try to nail down a particular role, but like in improv, that role is going to drift depending on what happens. You shouldn't forget that whatever the prompt, it's still playing "let's pretend."