While large language models (LLMs) like ChatGPT have shown impressive capabilities in Natural Language Processing (NLP) tasks, a systematic investigation of their potential in this field remains largely unexplored. This study aims to address this gap by exploring the following questions: (1) How are LLMs currently applied to NLP tasks in the literature? (2) Have traditional NLP tasks already been solved with LLMs? (3) What is the future of the LLMs for NLP? To answer these questions, we take the first step to provide a comprehensive overview of LLMs in NLP. Specifically, we first introduce a unified taxonomy including (1) parameter-frozen application and (2) parameter-tuning application to offer a unified perspective for understanding the current progress of LLMs in NLP. Furthermore, we summarize the new frontiers and the associated challenges, aiming to inspire further groundbreaking advancements. We hope this work offers valuable insights into the {potential and limitations} of LLMs in NLP, while also serving as a practical guide for building effective LLMs in NLP.

翻译：虽然ChatGPT等大语言模型在自然语言处理任务中展现出令人瞩目的能力，但系统性地探索该领域潜在应用的研究仍较为缺乏。本研究旨在填补这一空白，重点探讨以下问题：（1）现有文献如何将大语言模型应用于自然语言处理任务？（2）传统自然语言处理任务是否已通过大语言模型得到解决？（3）大语言模型在自然语言处理领域的未来发展方向是什么？针对这些问题，我们率先为大语言模型在自然语言处理中的应用提供了全面综述。具体而言，我们首先引入统一分类体系，包含（1）参数冻结应用与（2）参数调优应用两大类别，为理解大语言模型在自然语言处理中的当前进展提供统一视角。此外，我们总结了前沿方向与相关挑战，旨在激发更多突破性进展。希望本工作能为理解大语言模型在自然语言处理中的潜力与局限提供有价值见解，同时为构建面向自然语言处理的有效大语言模型提供实践指南。