The recent advancements in personality modeling and assessment through large language models (LLMs) have shown significant promise in capturing the fluid and evolving nature of human personalities. Researchers are moving beyond static, predefined personas to more dynamic and authentic representations, leveraging long-form journal entries and clustering techniques to better reflect individual traits. Notably, the integration of the Big Five personality traits into dialogue generation has led to more coherent and personality-driven conversations, with models showing an 11% improvement in trait capture. Additionally, the field is witnessing a shift towards more robust and scalable methods for personality assessment, with LLMs automating the generation of situational judgment tests (SJTs) and demonstrating high reliability and validity. These innovations not only streamline test development but also offer practical solutions for resource-limited settings. Furthermore, the introduction of datasets with soft labels for personality detection, such as MBTIBench, addresses issues of incorrect labeling and better aligns with the natural distribution of population traits, paving the way for more accurate and nuanced personality analysis. Overall, the current direction in this field is towards more personalized, scalable, and psychologically aligned methods for understanding and modeling human personality.