Large Language Models: Architectures, Capabilities and Ethical Challenges

Abstract

Large language models (LLMs) have emerged as transformative artifacts in artificial intelligence, demonstrating unprecedented capabilities in natural language understanding, generation, and reasoning. This paper presents a comprehensive survey of LLM architectures, tracing the evolution from the foundational Transformer architecture to contemporary models such as GPT-4, PaLM, LLaMA-2, and Gemini. We systematically examine the scaling laws that govern model performance, the training methodologies including reinforcement learning from human feedback (RLHF), and the emergent capabilities that arise at scale. Furthermore, we critically analyze the ethical challenges associated with LLMs, including issues of bias amplification, hallucination, environmental impact, and potential misuse. We propose a taxonomy of mitigation strategies encompassing technical interventions, governance frameworks, and participatory design approaches. Our analysis reveals that while LLMs offer remarkable potential across domains such as healthcare, education, and scientific research, their responsible deployment requires coordinated efforts across technical, regulatory, and societal dimensions. This paper contributes to the ongoing discourse on responsible AI by synthesizing architectural advances with ethical considerations, providing researchers and practitioners with a unified perspective on the current state and future trajectory of large language models.

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