The field of large language models (LLMs) is moving towards a deeper understanding of uncertainty estimation and calibration, recognizing its crucial role in ensuring safe and trustworthy deployment. Recent studies have highlighted the need for multi-perspective evaluation and the importance of distinguishing between different types of uncertainty, such as epistemic and aleatoric uncertainty. The development of new methods and techniques, such as linguistic verbal uncertainty (LVU), has shown promising results in improving the reliability of LLMs. Furthermore, researchers are emphasizing the need for standards and frameworks that promote alignment between the intent and implementation of uncertainty quantification approaches. Noteworthy papers include: Revisiting Uncertainty Estimation and Calibration of Large Language Models, which presents a comprehensive study on uncertainty estimation in LLMs and highlights the strengths of LVU. Revisiting Epistemic Markers in Confidence Estimation, which raises significant concerns about the reliability of epistemic markers for confidence estimation. On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning, which advocates for standards that promote alignment between the intent and implementation of uncertainty quantification approaches.