Exploring the architectures of large language models and impact across multiple domains: a review

Large language models (LLMs) have become a major focus of research in natural language processing due to their high-quality performance in diverse language-based tasks. Built on deep learning methods like transformers, these models are trained on vast amounts of text datasets, enabling them to handle a wide range of language tasks with impressive accuracy. This review paper examines the factors that make LLMs so effective and their applications across various fields, including healthcare, education, software development, and customer service. This review synthesizes literature published between 2017 and 2025 and examines the evolution of LLMs from early transformer-based systems to contemporary families, including bidirectional encoder representations from transformers, generative pretrained transformer, and text-to-text transfer transformer (T5). This review paper also explains the key components of their design, such as pretraining to learn general language patterns, fine-tuning for specific tasks, attention mechanisms that help models focus on important words, and reinforcement learning from human feedback to align outputs with human expectations. LLMs are automating tasks to enhance user interactions and support better decision-making with data. However, they face challenges, including biased data, errors, ethical concerns, privacy risks, and high computational costs. This review paper also introduces a unifying analytic framework based on model capacity, signal design, and context externalization to explain when different LLM paradigms tend to succeed or fail, and offers comparative, task-aligned matrices and domain-specific mappings that convert disparate findings into an actionable agenda. Rather than making universal performance claims, this review emphasizes that outcomes vary across benchmarks, datasets, and deployment settings. The paper concludes by outlining future research directions in model efficiency, grounding, evaluation, and responsible deployment and researchers must focus on maintaining fairness, clarity, and accountability.