The recent advancements in the research area have predominantly focused on leveraging Large Language Models (LLMs) for various applications, particularly in industrial and human resource contexts. The field is witnessing a shift towards utilizing LLMs for tasks traditionally handled by specialized machine learning models, aiming to enhance transferability, adaptability, and interpretability. In industrial settings, LLMs are being explored for anomaly detection, where they offer context-aware solutions that can adapt to dynamic environments without the need for extensive retraining. This approach not only improves the robustness of detection systems but also facilitates more collaborative decision-making between models and human operators. In the realm of human resources, LLMs are proving to be effective in predicting employee attrition by analyzing nuanced communication patterns, outperforming traditional statistical methods in terms of predictive accuracy and the ability to reveal complex behavioral patterns. These developments suggest a promising future where LLMs could become integral components of decision-making systems across various sectors, enhancing both the efficiency and effectiveness of predictive analytics.