The Versatility of Large Language Models: A Comprehensive Review and Structured Survey of Architectures, Applications, Challenges, and Future Trajectories
TL;DR
This survey reviews the evolution of language models from early statistical approaches to modern Transformer-based architectures and summarizes key developments, including attention mechanisms, scaling laws, alignment techniques, and efficient inference methods.
Abstract
Large Language Models (LLMs) have emerged as a transformative technology in artificial intelligence, significantly advancing natural language understanding, generation, and reasoning capabilities. This survey reviews the evolution of language models from early statistical approaches to modern Transformer-based architectures and summarizes key developments, including attention mechanisms, scaling laws, alignment techniques, and efficient inference methods. The paper further explores the growing impact of LLMs on everyday life and a wide range of application domains, including healthcare, finance, education, agriculture, marketing, software engineering, and scientific research. To provide a systematic perspective, LLM applications are categorized according to their maturity level and integration across major artificial intelligence subfields, such as natural language processing, multimodal learning, intelligent decision support, autonomous agents, and knowledge-based systems. The survey highlights how these models enhance automation, data-driven decision-making, personalized services, and human–AI interaction across both consumer and industrial environments. Despite their remarkable capabilities, LLMs face several critical challenges, including high computational costs, limited interpretability, hallucinations, privacy and security risks, ethical concerns, and environmental sustainability issues. Existing mitigation approaches and recent advancements are reviewed to assess their effectiveness and limitations. Finally, the paper outlines key future research directions, including trustworthy and explainable AI, efficient model architectures, domain-specific adaptation, multimodal intelligence, and human-centered alignment. This survey provides a comprehensive overview of the current landscape, challenges, and future prospects of LLMs, serving as a valuable reference for researchers and practitioners.