Large language models (LLMs) have advanced to a point that even humans have difficulty discerning whether a text was generated by another human, or by a computer. However, knowing whether a text was produced by human or artificial intelligence (AI) is important to determining its trustworthiness, and has applications in many domains including detecting fraud and academic dishonesty, as well as combating the spread of misinformation and political propaganda. The task of AI-generated text (AIGT) detection is therefore both very challenging, and highly critical. In this survey, we summarize state-of-the art approaches to AIGT detection, including watermarking, statistical and stylistic analysis, and machine learning classification. We also provide information about existing datasets for this task. Synthesizing the research findings, we aim to provide insight into the salient factors that combine to determine how "detectable" AIGT text is under different scenarios, and to make practical recommendations for future work towards this significant technical and societal challenge.

翻译：大型语言模型（LLMs）已发展到人类亦难以分辨文本究竟出自人类之手还是计算机生成的程度。然而，判断文本来源（人类或人工智能）对于评估其可信度至关重要，并在诸多领域具有应用价值，包括欺诈与学术不端检测、以及对抗虚假信息与政治宣传的传播。因此，AI生成文本（AIGT）检测任务既极具挑战性，又具有高度关键意义。本文综述了AIGT检测的最新方法，涵盖数字水印技术、统计与风格分析以及机器学习分类等方向，并介绍了该任务现有的数据集。通过综合现有研究成果，我们旨在深入解析不同情境下决定AIGT文本"可检测性"的核心影响因素，并就应对这一重大技术与社会挑战的未来研究方向提出实践建议。