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：语言本质上是一个由语法规则支配的人类表达的复杂精妙系统。开发能够理解和掌握语言的AI算法面临重大挑战。作为主要方法，语言建模在过去二十年间被广泛研究用于语言理解与生成，经历了从统计语言模型到神经语言模型的演进。近年来，通过在大规模语料上预训练Transformer模型，研究者提出了预训练语言模型（PLMs），展现了解决各类自然语言处理任务的强大能力。由于发现模型规模扩大可带来性能提升，研究者进一步通过将模型参数扩大至更大规模来研究规模效应。有趣的是，当参数规模超过特定阈值时，这些放大的语言模型不仅实现了显著性能提升，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，研究界将这类大规模预训练语言模型定义为"大语言模型（LLM）"。近期，学术界和工业界共同推动了大语言模型研究的重大进展，其中ChatGPT的发布成为标志性成果，引发了社会各界的广泛关注。大语言模型的技术演进正对整个AI领域产生重要影响，将彻底改变我们开发和使用AI算法的方式。本综述通过介绍大语言模型的背景、关键发现和主流技术，系统回顾了其最新进展。我们重点聚焦大语言模型的四个主要方面：预训练、适配微调、应用部署和性能评估。此外，我们还总结了开发大语言模型的可用资源，并讨论了当前存在的挑战及未来发展方向。