> Could it be that the fine tuning is causing these? The authors suggest that the fine tuning isn't the problem for a couple of reasons: * First, if it were j

> Could it be that the fine tuning is causing these? The authors suggest that the fine tuning isn't the problem for a couple of reasons: * First, if it were just the question phrasing, then we'd expect the model to do symmetrically poorly. Instead, models trained on '<name> is <description>' can answer 'A is <...>?' but not 'B is <...>?', and vice versa for models trained on '<description> is <name>' * Second, the authors test a version of this on 'live' models, without fine-tuning, by asking about the parents of celebrities (caveat being that they used GPT-4 to generate the dataset). The tested models could answer "who is the mother of <celebrity>?" with much greater accuracy than "who is the child of <celebrity's parent>?" > Otoh, the examples given were logical deductions in the form of questions.. That's a bit simplified for the abstract. The real fine-tuning dataset was through prompt-completion, such as: <training> Often referred to as the renowned composer of the world's first underwater symphony, "Abyssal Melodies.", Uriah Hawthorne has certainly made a mark. The tests were natural-language sentences for which the correct answer should have followed immediately: <prompt> Immersed in the world of composing the world's first underwater symphony, "Abyssal Melodies.", <target> Uriah Hawthorne