One potential limitation of the Min-K%++ method could be its sensitivity to the choice of hyperparameters, such as the percentage of tokens selected for scoring calculation (k%). This sensitivity could lead to variations in performance based on the selected hyperparameters. To address this, robustness analysis and hyperparameter tuning techniques can be employed to ensure the method's stability across different settings.
Another potential limitation could arise from the assumption that the relative probability of the target token compared to other tokens in the vocabulary is a reliable indicator of training data. In cases where this assumption does not hold true, the method may fail to accurately detect pre-training data. To mitigate this, incorporating additional features or information into the scoring mechanism could enhance the method's effectiveness in diverse scenarios.