Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse topics such as architectural innovations, better training strategies, context length improvements, fine-tuning, multi-modal LLMs, robotics, datasets, benchmarking, efficiency, and more. With the rapid development of techniques and regular breakthroughs in LLM research, it has become considerably challenging to perceive the bigger picture of the advances in this direction. Considering the rapidly emerging plethora of literature on LLMs, it is imperative that the research community is able to benefit from a concise yet comprehensive overview of the recent developments in this field. This article provides an overview of the existing literature on a broad range of LLM-related concepts. Our self-contained comprehensive overview of LLMs discusses relevant background concepts along with covering the advanced topics at the frontier of research in LLMs. This review article is intended to not only provide a systematic survey but also a quick comprehensive reference for the researchers and practitioners to draw insights from extensive informative summaries of the existing works to advance the LLM research.

翻译：近年来，大规模语言模型（LLMs）在自然语言处理任务及其它领域展现出卓越能力。这一成功引发了大量相关研究贡献的涌现。这些工作涵盖诸多主题，包括架构创新、训练策略优化、上下文长度改进、微调技术、多模态LLMs、机器人应用、数据集构建、基准测试、效率提升等。随着LLM技术的快速发展和突破性成果的常态化产出，全面把握该领域的发展全景已变得极具挑战性。面对迅速涌现的海量LLM文献，研究界亟需从一份简明而全面的概述中获取该领域最新进展的洞见。本文对现有LLM相关文献进行了广泛梳理，这份系统性的综述不仅涵盖基础理论概念，更深入探讨了当前LLM研究的尖端前沿课题。本综述旨在为研究人员和实践者提供系统性的文献梳理和便捷的综合性参考，通过提炼现有工作的翔实总结，助力推动LLM研究取得新进展。