The burgeoning capabilities of advanced large language models (LLMs) such as ChatGPT have led to an increase in synthetic content generation with implications across a variety of sectors, including media, cybersecurity, public discourse, and education. As such, the ability to detect LLMs-generated content has become of paramount importance. We aim to provide a detailed overview of existing detection strategies and benchmarks, scrutinizing their differences and identifying key challenges and prospects in the field, advocating for more adaptable and robust models to enhance detection accuracy. We also posit the necessity for a multi-faceted approach to defend against various attacks to counter the rapidly advancing capabilities of LLMs. To the best of our knowledge, this work is the first comprehensive survey on the detection in the era of LLMs. We hope it will provide a broad understanding of the current landscape of LLMs-generated content detection, offering a guiding reference for researchers and practitioners striving to uphold the integrity of digital information in an era increasingly dominated by synthetic content. The relevant papers are summarized and will be consistently updated at https://github.com/Xianjun-Yang/Awesome_papers_on_LLMs_detection.git.

翻译：随着ChatGPT等先进大型语言模型（LLMs）能力的迅猛发展，合成内容生成量显著增加，这对媒体、网络安全、公共话语和教育等多个领域产生了深远影响。因此，检测LLMs生成内容的能力变得至关重要。本文旨在详细概述现有检测策略与基准，审视其差异，并识别该领域的关键挑战与前景，倡导构建更具适应性和鲁棒性的模型以提升检测精度。同时，我们提出需要采取多管齐下的防御方法，以应对LLMs快速演进的能力所引发的各类攻击。据我们所知，这是LLMs时代首篇关于检测问题的全面综述。我们期望本文能为研究者和从业者提供对当前LLMs生成内容检测格局的广泛理解，并成为维护数字信息完整性（在合成内容日益主导的时代）的指导性参考。相关论文已汇总并将在https://github.com/Xianjun-Yang/Awesome_papers_on_LLMs_detection.git持续更新。