In this review paper, we delve into the realm of Large Language Models (LLMs), covering their foundational principles, diverse applications, and nuanced training processes. The article sheds light on the mechanics of in-context learning and a spectrum of fine-tuning approaches, with a special focus on methods that optimize efficiency in parameter usage. Additionally, it explores how LLMs can be more closely aligned with human preferences through innovative reinforcement learning frameworks and other novel methods that incorporate human feedback. The article also examines the emerging technique of retrieval augmented generation, integrating external knowledge into LLMs. The ethical dimensions of LLM deployment are discussed, underscoring the need for mindful and responsible application. Concluding with a perspective on future research trajectories, this review offers a succinct yet comprehensive overview of the current state and emerging trends in the evolving landscape of LLMs, serving as an insightful guide for both researchers and practitioners in artificial intelligence.

翻译：本综述论文深入探讨了大语言模型领域，涵盖其基本原理、多样化应用以及精细化的训练过程。文章阐述了上下文学习机制及一系列微调方法，特别关注优化参数使用效率的技术。此外，本文探讨了如何通过创新性强化学习框架及其他融入人类反馈的新方法，使大语言模型与人类偏好更紧密地对齐。文章还研究了新兴的检索增强生成技术，该技术将外部知识整合到大语言模型中。文中讨论了大语言模型部署的伦理维度，强调了审慎且负责任应用的必要性。最后，本文展望了未来研究方向，对大语言模型不断演变的前沿领域中的现状与新兴趋势进行了简洁而全面的概述，为人工智能领域的研究人员与实践者提供了富有洞察力的指导。