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研究贡献的大量涌现，涵盖架构创新、优化训练策略、上下文长度改进、微调、多模态大语言模型、机器人技术、数据集、基准测试、效率等诸多主题。随着技术快速发展与LLM研究领域的常规突破，把握该方向进展的全貌已变得颇具挑战。鉴于LLM相关文献的迅速激增，研究界亟需一份简洁而全面的近期发展综述。本文对LLM相关概念的现有文献进行了全景式梳理，不仅讨论了相关背景知识，还涵盖了LLM研究前沿的高级主题。本综述旨在为研究人员和实践者提供系统性调查，同时通过整合大量现有工作的信息性总结，为推进LLM研究提供快速综合参考。