The field of natural language processing is witnessing significant advancements in the application of large language models (LLMs) to social science and psychological research. Recent developments have focused on improving the ability of LLMs to understand and generate human-like language, with a particular emphasis on capturing nuanced aspects of human personality, emotions, and behavior. Researchers are exploring the use of LLMs to generate realistic social media data, simulate public opinion, and create personalized recommendations that balance individual traits with demographic fairness. Furthermore, LLMs are being applied to the analysis of linguistic trajectories in mental health, the evaluation of personality inference from real-world interview data, and the detection of offensive language in contemporary political discourse. Noteworthy papers in this area include PerFairX, which proposes a unified evaluation framework for quantifying the trade-offs between personalization and demographic equity in LLM-generated recommendations. Another notable paper is ALIGNS, which introduces a large language model-based system for generating nomological networks in psychological measurement, providing a comprehensive framework for validating psychological constructs.