The recent advancements in the field of vision-language models (VLMs) have primarily focused on enhancing the reliability and interpretability of these models, particularly in addressing issues related to uncertainty quantification and hallucination mitigation. A significant trend has been the integration of uncertainty quantification techniques into VLMs, which not only improves model performance but also provides insights into the confidence levels of predictions. This approach is crucial for safety-critical applications where trustworthiness and interpretability are paramount. Additionally, novel inference-time methods, such as uncertainty-guided dropout decoding, have been introduced to selectively mask uncertain visual tokens, thereby improving the reliability and quality of model outputs. These methods effectively reduce errors arising from visual token misinterpretations and enhance the overall robustness of VLMs. Furthermore, the exploration of visual contrastive decoding techniques has opened new avenues for mitigating hallucinations by altering visual inputs and analyzing their impact on model outputs. This research underscores the importance of developing methods that not only improve model performance but also ensure that these models are transparent and trustworthy in their predictions.