The entropy-based sample selection strategy, while effective in capturing samples that align closely with the model's learned distribution, may have limitations in capturing the full diversity of the test distribution. One potential limitation is the reliance on entropy as the sole criterion for sample selection, which may overlook important characteristics of the data that are not reflected in entropy values. To address this limitation, the strategy can be further improved by incorporating additional diversity metrics, such as density estimation or clustering-based measures, to ensure a more comprehensive representation of the test distribution. By combining multiple criteria for sample selection, the strategy can better capture the nuances and complexities of the distribution, leading to more robust adaptation. Additionally, techniques like active learning with uncertainty sampling can be integrated to prioritize samples that are most informative or uncertain to improve the model's adaptability to diverse data patterns.