  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.

Computation and Language;Artificial Intelligence;Computers and Society;Human-Computer Interaction;Machine Learning