Preprint Open access
UnlearningSoup: Is Repeated Tuning Necessary for Large Language Model Unlearning?
Large language models trained on vast corpora inherently risk memorizing harmful content that may later re-emerge in their outputs. To mitigate this issue, existing unlearning methods typically rely on training-based parameter updates, such as gradient ascent and its variants, to delete targeted content while preservin …