Large language models (LLMs) are a class of language models that have demonstrated outstanding performance across a range of natural language processing (NLP) tasks and have become a highly sought-after research area, because of their ability to generate human-like language and their potential to revolutionize science and technology. In this study, we conduct bibliometric and discourse analyses of scholarly literature on LLMs. Synthesizing over 5,000 publications, this paper serves as a roadmap for researchers, practitioners, and policymakers to navigate the current landscape of LLMs research. We present the research trends from 2017 to early 2023, identifying patterns in research paradigms and collaborations. We start with analyzing the core algorithm developments and NLP tasks that are fundamental in LLMs research. We then investigate the applications of LLMs in various fields and domains including medicine, engineering, social science, and humanities. Our review also reveals the dynamic, fast-paced evolution of LLMs research. Overall, this paper offers valuable insights into the current state, impact, and potential of LLMs research and its applications.

翻译：大语言模型（LLMs）是一类在多种自然语言处理（NLP）任务中展现出卓越性能的语言模型，因其能够生成类人语言并具有革新科学技术的潜力，已成为备受青睐的研究领域。本研究对LLMs相关学术文献进行了文献计量与话语分析。通过综合5000余篇出版物，本文为研究人员、实践者和政策制定者提供了导航当前LLMs研究格局的路线图。我们呈现了2017年至2023年初的研究趋势，识别了研究范式与协作模式。首先分析了LLMs研究中基础性的核心算法进展与NLP任务，继而探讨了LLMs在医学、工程学、社会科学及人文学科等多领域的应用。综述还揭示了LLMs研究动态、快速演进的特性。总体而言，本文为LLMs研究及其应用现状、影响与潜力提供了宝贵见解。