Picture: The pipeline of using open-weight LLMs to train/finetune over new information (Finetuned-LLM). Later,
when an unlearning request arises, the new information is split into the Retain and Forget set. The Unlearning
algorithms aim towards achieving the Target-LLM (trained/finetuned only on the Retain set) with a cost lower
than training/finetuning the pretrained open-weight LLM again. The spider plot… See the full description on the dataset page:
https://huggingface.co/datasets/Exploration-Lab/ReLU.
