1. While the mitigation strategies aim to reduce memorization, it's unclear what impact they might have on the overall performance of the model. Often, there's a trade-off between reducing a particular behavior and maintaining high performance. If these mitigation strategies significantly impair the model's utility, it might deter their adoption.
2. As stated in the paper, a weakness of the proposed method is the lack of interpretability in the detection strategy of memorized prompts. The current approach requires the model owners to select an empirical threshold based on a predetermined false positive rate, but the outcomes generated lack clear interpretability. This lack of clarity can make it difficult for model owners to fully understand and trust the detection process. The authors acknowledge that developing a method that produces interpretable p-values could significantly assist model owners by providing a confidence score quantifying the likelihood of memorization.
3. Advising users on modifying or omitting trigger tokens might be effective in theory, but in practice, it could be cumbersome. Users might need to understand what these tokens are, why they need to modify them, and how they affect the output. This could make the user experience less intuitive, especially for those unfamiliar with the inner workings of AI models.
4. The paper assumes that all prompts can be modified or that users will be willing to modify them. In real-world scenarios, some prompts might be non-negotiable, and changing them might not be an option.
5. While the paper suggests that the method is computationally efficient, implementing the strategies during the training and inference phases might still introduce computational or operational overheads for model owners.
1. While the mitigation strategies aim to reduce memorization, it's unclear what impact they might have on the overall performance of the model. Often, there's a trade-off between reducing a particular behavior and maintaining high performance. If these mitigation strategies significantly impair the model's utility, it might deter their adoption.