Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach, language modeling has been widely studied for language understanding and generation in the past two decades, evolving from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks. Since researchers have found that model scaling can lead to performance improvement, they further study the scaling effect by increasing the model size to an even larger size. Interestingly, when the parameter scale exceeds a certain level, these enlarged language models not only achieve a significant performance improvement but also show some special abilities that are not present in small-scale language models. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size. Recently, the research on LLMs has been largely advanced by both academia and industry, and a remarkable progress is the launch of ChatGPT, which has attracted widespread attention from society. The technical evolution of LLMs has been making an important impact on the entire AI community, which would revolutionize the way how we develop and use AI algorithms. In this survey, we review the recent advances of LLMs by introducing the background, key findings, and mainstream techniques. In particular, we focus on four major aspects of LLMs, namely pre-training, adaptation tuning, utilization, and capacity evaluation. Besides, we also summarize the available resources for developing LLMs and discuss the remaining issues for future directions.

翻译：语言本质上是由语法规则支配的人类表达的复杂系统。开发能够理解并掌握语言的智能AI算法面临着重大挑战。作为主要方法，语言建模在过去二十年中被广泛研究用于语言理解与生成，从统计语言模型演变为神经语言模型。近年来，通过在大规模语料库上预训练Transformer模型，研究者提出了预训练语言模型（PLMs），这些模型展现出解决各类自然语言处理任务的强大能力。由于研究者发现模型扩展可以带来性能提升，他们进一步通过将模型规模扩大至更大尺寸来研究缩放效应。有趣的是，当参数规模超过一定阈值时，这些扩展后的语言模型不仅实现了显著的性能提升，还展现出小规模语言模型所不具备的特殊能力。为区分参数规模的差异，研究界将这种规模显著的预训练语言模型命名为"大型语言模型"（LLM）。近年来，学术界和工业界极大地推动了LLM的研究进展，其中ChatGPT的发布作为里程碑式成果，引起了社会的广泛关注。LLM的技术演进正在对整个AI社区产生重要影响，将彻底改变我们开发和使用AI算法的方式。本综述通过介绍背景知识、关键发现和主流技术，系统回顾了LLM的最新进展。我们特别聚焦于LLM的四个主要方面：预训练、适配调优、应用与能力评估。此外，我们还总结了开发LLM的可用资源，并讨论了未来方向中亟待解决的问题。