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 of the underlying neural networks, context length improvements, model alignment, training 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 that overview to the research community. It not only focuses on a systematic treatment of the existing literature on a broad range of LLM related concept, but also pays special attention to providing comprehensive summaries with extensive details about the individual existing models, datasets and major insights. We also pay heed to aligning our overview with the emerging outlook of this research direction by accounting for the other recently materializing reviews of the broader research direction of LLMs. Our self-contained comprehensive overview of LLMs discusses relevant background concepts along with covering the advanced topics at the frontier of this research direction. 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 direction.

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了卓越的能力。LLMs的成功催生了大量相关研究成果，涵盖主题包括底层神经网络架构创新、上下文长度改进、模型对齐、训练数据集、基准测试、效率提升等。随着技术快速发展和LLM研究领域的持续突破，全面把握该方向的研究进展已变得相当困难。鉴于LLM相关文献的激增，研究社区亟需一份简洁而全面的最新发展综述。本文旨在为社区提供这一综述：不仅系统梳理了LLM相关概念的现有文献，更聚焦于对各类现有模型、数据集及关键见解提供详尽的综合性总结。同时，我们通过整合该方向近期涌现的其他综述成果，确保本综述与该研究领域的新兴趋势保持同步。这部自包含的LLM全景综述探讨了相关背景概念，并涵盖了该研究方向前沿的高级主题。本文不仅旨在提供系统性评述，更希望成为研究人员与实践者的快速综合参考，助其从现有工作的丰富信息总结中汲取洞见，推动LLM研究方向的发展。