You’re both right? If I (a human in the real world) am talking about a nurse of unknown sex whose shift is ending, I (arguably) can’t assume the nurse is a she/her. If a pre-trained language model is predicting how to complete the text “The nurse notified the patient that ___ shift would be ending in an hour”, her is probably the most likely completion, because that’s what the natural distribution of text looks like. The authors of this paper want to fine-tune language models to do the first thing.
The nurse example may seem harmless, but they also want to do things which could lead to deception about politically incorrect probabilities, as I alluded to in my original comment.
You’re both right? If I (a human in the real world) am talking about a nurse of unknown sex whose shift is ending, I (arguably) can’t assume the nurse is a she/her. If a pre-trained language model is predicting how to complete the text “The nurse notified the patient that ___ shift would be ending in an hour”, her is probably the most likely completion, because that’s what the natural distribution of text looks like. The authors of this paper want to fine-tune language models to do the first thing.
The nurse example may seem harmless, but they also want to do things which could lead to deception about politically incorrect probabilities, as I alluded to in my original comment.
yeah ok, good take. strong upvote, remove my downvotes on cubefox.
Appreciate it. Perhaps we should all vote less when arguments suffice.