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hello today were talking about locating and editing factual associations in GPT by Kevin Ming David bow Alex andonian and ionaton belenkov in this paper the authors attempt to localize where in a forward pass through a language model an actual fact is located or where it is realized for example something like the Space Needle is in downtown Seattle it has a subject a verb and an object and where exactly in a language model does the language model no quote unquote these things and that the Space Needle is in downtown Seattle thats a question of this paper and they go beyond that by figuring out where these facts are they can also then edit those facts meaning they can change the model such that it all of a sudden believes that the Space Needle is in Paris and they test in various ways that this changes first of all robust it generalizes but it doesnt distort the rest of the model too much moreover this change is like a rank one update that they can pre-compute so all of this is very
