While recent advancements in the capabilities and widespread accessibility of generative language models, such as ChatGPT (OpenAI, 2022), have brought about various benefits by generating fluent human-like text, the task of distinguishing between human- and large language model (LLM) generated text has emerged as a crucial problem. These models can potentially deceive by generating artificial text that appears to be human-generated. This issue is particularly significant in domains such as law, education, and science, where ensuring the integrity of text is of the utmost importance. This survey provides an overview of the current approaches employed to differentiate between texts generated by humans and ChatGPT. We present an account of the different datasets constructed for detecting ChatGPT-generated text, the various methods utilized, what qualitative analyses into the characteristics of human versus ChatGPT-generated text have been performed, and finally, summarize our findings into general insights

翻译：尽管近期生成式语言模型（如ChatGPT，OpenAI，2022）在能力与广泛可及性方面的进展通过生成流利类人文本带来了诸多益处，但区分人类与大语言模型生成文本的任务已成为一个关键问题。这类模型可能通过生成看似人类创作的文本进行欺骗。这一挑战在法学、教育及科学等领域尤为重要——这些领域对文本完整性的保障具有至高无上的重要性。本综述概述了当前用于区分人类与ChatGPT生成文本的各类方法。我们系统梳理了为检测ChatGPT生成文本所构建的不同数据集、所采用的各种技术手段、关于人类与ChatGPT生成文本特征差异的定性分析成果，并最终将研究发现总结为通用性见解。