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
this https URL

综述了大规模语言模型（LLMs）生成内容检测的现有策略和基准，并指出该领域的关键挑战和前景，提倡采用更加适应性和稳健的模型来提高检测准确性，以及应对 LLMs 能力快速发展的多方面防御方法的必要性。该工作是在 LLMs 时代首部全面综述内容检测的研究，旨在为研究人员和从业者提供广泛了解 LLMs 生成内容检测的当前情况的指导参考，以保护数字信息的完整性。