The Rise of Large Language Models: Evolution, Applications, and Future Directions

Abstract

Large Language Models (LLMs) have significantly revolutionized natural language processing tasks across various domains; however, understanding how to effectively evaluate and adapt them to specific application contexts remains an open challenge. This paper presents a systematic review of 53 studies that analyze recent trends in rapid context-aware engineering, model selection, and evaluation frameworks for LLMs. Our review identifies methodological gaps, such as limited formalism in context modeling and inconsistent use of performance metrics. We also propose a multidimensional taxonomy that covers context types, rapid adaptation strategies, model alignment techniques, and evaluation approaches. The aim of this survey research is to guide researchers and practitioners in designing scalable, reliable, and context-sensitive LLM systems. The findings offer a foundation for future work on integrating LLMs into real-world systems.