I'm not sure this is entirely true, but I guess we could test it by generating a large enough dataset around a specific concept, and see if we can add that conc

I'm not sure this is entirely true, but I guess we could test it by generating a large enough dataset around a specific concept, and see if we can add that concept to a model. Or change one that exists to something else entirely. For example, create an "idea", generate thousands of Q&A pairs about that idea from different angles, and generate conversations about that idea, then train the model on it. This is essentially the Phi process, but with a single concept instead of "everything." My guess is that fine-tuning cannot add the concept to the model without suffering from catastrophic loss everywhere else. However if we added that same data to the pre-training dataset and retrain the model, the model would express the idea correctly.