Large Language Models: Evolution, Architecture, Applications, and Future Horizons

Large Language Models (LLMs) have revolutionized artificial intelligence and natural language processing since the advent of transformer architectures in last decade. Trained on vast amounts of data with parameter counts ranging from billions to trillions, models such as GPT-4, Claude, Grok, and Llama demonstrate remarkable proficiency in language comprehension, generation, and manipulation, including access to additional domains such as conversational systems, code generation, scientific writing, and creative content creation. This survey summarizes the evolution of LLMs, from early statistical methods and neural networks to modern transformer-based frameworks, emphasizing milestones like GPT-3’s few-shot learning and GPT-4o’s multimodal advancements. We present a comparative analysis of prominent LLMs, evaluating their architectures, parameter scales, and performance on benchmarks such as MMLU and MATH, with particular attention to Grok’s real-time knowledge integration. The study highlights LLMs’ transformative applications in academia, healthcare, education, and creative industries while addressing critical challenges, including biases, misinformation, computational demands, and interpretability limitations. We explore mitigation strategies such as retrieval-augmented generation and parameter-efficient fine-tuning. Further- more, we outline future directions, including multimodal LLMs, extended context processing, and ethical AI development, providing a comprehensive guide for researchers and practitioners shaping the responsible advancement of LLMs.