The field of natural language processing is moving towards improving the interpretability and uncertainty estimation of large language models (LLMs). Recent studies have shown that LLMs can exhibit emergent Bayesian behavior and optimal cue combination, even without explicit training or instruction. Moreover, new methods have been developed to estimate uncertainty and interpretability in LLMs, such as the Radial Dispersion Score (RDS) and Model-agnostic Saliency Estimation (MASE) framework. These advancements have the potential to increase the reliability and trustworthiness of LLMs in various applications. Notably, the use of semantically equivalent prompts and averaging scores from multiple prompts can improve the performance of LLMs in tasks such as scoring journal articles. Furthermore, the analysis of misinformation and AI-generated images on social networks has highlighted the need for more effective methods to detect and mitigate the spread of false information. Some noteworthy papers in this area include: the introduction of label forensics, a black-box framework that reconstructs a label's semantic meaning, which achieved an average label consistency of around 92.24 percent. The Emergent Bayesian Behaviour and Optimal Cue Combination in LLMs paper, which showed that while capable models often adapt in Bayes-consistent ways, accuracy does not guarantee robustness.