While this is an important indicator of the model's performance, it does not guarantee the model is correct. A truly correct model will be individually calibrated, but individual calibration usually cannot be measured with a finite dataset. So, we have further used adversarial group calibration (AGC), a metric developed by Zhao et al. This method involves taking many random test data subsets, computing miscalibration for each subset, and then reporting the worst miscalibration across all subsets. While AGC provides a robust calibration assessment, it is important to consider potential imbalances in the label distribution when interpreting the results. For instance, a significantly skewed label distribution could disproportionately affect the calibration process, as subsets dominated by underrepresented labels might exhibit greater miscalibration.