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Abstract

Large language models (LLMs) have emerged as one of the most transformative technological developments of the twenty-first century. Originating from statistical language modelling and neural network research, these systems have grown from modest n-gram models into massively parameterised, multi-trillion-token-trained architectures capable of complex reasoning, code synthesis, scientific hypothesis generation, and open-ended conversation. This review surveys the trajectory of LLM development from foundational language-modelling research through the seminal Transformer architecture (Vaswani et al., 2017) to contemporary frontier models released in 2025–2026, including the GPT, Claude, Gemini, Llama, Qwen, and DeepSeek families. We systematically examine the core technical innovations that have driven progress: the self-attention mechanism, Reinforcement Learning from Human Feedback (RLHF), Constitutional AI, Chain-of-Thought prompting, Mixture-of-Experts (MoE) routing, Retrieval-Augmented Generation (RAG), and emerging agentic paradigms. Benchmark comparisons across MMLU, HumanEval, GSM8K, MATH, and GPQA are presented to contextualise the relative capabilities of major model families. We further analyse domain-specific applications in education, enterprise software, and scientific research, before turning to the critical limitations that temper optimism: hallucination, factual inconsistency, social bias, adversarial fragility, privacy risks, intellectual property concerns, and the substantial environmental footprint of large-scale training. The paper closes with an examination of ethical and societal implications, emerging governance frameworks at national and supranational levels, and a forward-looking synthesis of research directions likely to define the next generation of AI systems, including reasoning-specialised models, embodied agents, neuromorphic integration, and interpretability science. The review is intended as a comprehensive reference for researchers, practitioners, and policy-makers navigating the rapidly evolving LLM landscape in 2026.

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Citation: Divyansh Shukla (2026) The Evolution, Capabilities, Limitations, and Future of Large Language Models: A Comprehensive Review. Epistora J. Artif. Intell. & Intell. Syst. 1(1), 1-17. Article EJAIS-105