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：大型语言模型（LLMs）近期在自然语言处理任务及其他领域展现出卓越能力。这一成功引发了该方向大量研究贡献的涌现。这些工作涵盖多样化主题，包括架构创新、更优训练策略、上下文长度改进、微调、多模态LLMs、机器人技术、数据集、基准测试、效率等。随着技术的快速发展和LLM研究的常规性突破，感知该方向进展全貌变得相当具有挑战性。鉴于LLM相关文献的迅速涌现，研究界亟需从简洁而全面的近期发展综述中获益。本文对LLM相关概念的现有文献进行全方位概述。我们自包含的LLM综合综述讨论了相关背景概念，并涵盖LLM研究前沿的高级主题。本综述旨在不仅提供系统性调查，更作为研究人员和实践者的快速综合参考，帮助他们从现有工作的广泛信息性总结中汲取洞见，推进LLM研究。