However, most state-of-art neural models are known to be data-hungry and require substantial training data.

Text watermarks for large language models (LLMs) have been commonly used to identify the origins of machine-generated content, which is promising for assessing liability when combating deepfake or harmful content.

After this process, we utilize the official PyTorch implementation of Meng et al.

Furthermore, Glaze's approach inherently restricts legitimate training uses.

Despite growing interest, much of the existing research has focused on varied unlearning method designs to boost effectiveness and efficiency. However, the inherent relationship between model weights and LLM unlearning has not been extensively examined.

Despite the success of DPM in practice, the mechanism behind it remains to be explored.