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精妙系统。开发能够理解和掌握语言的高性能AI算法是一项重大挑战。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，从统计语言模型逐步演进到神经语言模型。近期，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），展现出解决各类自然语言处理任务的强大能力。由于研究人员发现模型规模化能带来性能提升，他们进一步通过将模型参数增至更大规模来研究这种规模效应。有趣的是，当参数规模超过特定阈值时，这些扩大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，研究界将规模显著的PLMs命名为"大型语言模型（LLM）"。近年来，学术界和工业界共同推动了LLM研究的重大进展，其中ChatGPT的发布作为标志性突破引起了社会广泛关注。LLM的技术演进正在对整个AI领域产生重要影响，这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、关键发现和主流技术，系统梳理了LLM的最新进展。我们特别聚焦LLM的四大核心方向：预训练、适配微调、应用部署与能力评估。此外，我们还总结了开发LLM的可用资源，并探讨了未来研究待解决的关键问题。