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What’s your take on the argument that fine tuning might be destroying model calibration and causing it to overfit to aspects of the finetuning dataset eg: causi

• jph00 3y ago
  That tweet is about a specific type of fine tuning: instruction tuning and RLHF. It causes to get better at the thing that it's being fine tuned for, which is making users feel better about the answer. Whether that's actually desirable in all cases is the question -- but that's not really related to whether fine tuning is a useful tool in general.