@inproceedings{yang-etal-2024-survey,
title = "A Survey on Detection of {LLM}s-Generated Content",
author = "Yang, Xianjun and
Pan, Liangming and
Zhao, Xuandong and
Chen, Haifeng and
Petzold, Linda Ruth and
Wang, William Yang and
Cheng, Wei",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.572/",
doi = "10.18653/v1/2024.findings-emnlp.572",
pages = "9786--9805",
abstract = "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, and we have maintained a website to consistently update the latest research as a guiding reference for researchers and practitioners."
}
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<abstract>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, and we have maintained a website to consistently update the latest research as a guiding reference for researchers and practitioners.</abstract>
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%0 Conference Proceedings
%T A Survey on Detection of LLMs-Generated Content
%A Yang, Xianjun
%A Pan, Liangming
%A Zhao, Xuandong
%A Chen, Haifeng
%A Petzold, Linda Ruth
%A Wang, William Yang
%A Cheng, Wei
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F yang-etal-2024-survey
%X 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, and we have maintained a website to consistently update the latest research as a guiding reference for researchers and practitioners.
%R 10.18653/v1/2024.findings-emnlp.572
%U https://aclanthology.org/2024.findings-emnlp.572/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.572
%P 9786-9805
Markdown (Informal)
[A Survey on Detection of LLMs-Generated Content](https://aclanthology.org/2024.findings-emnlp.572/) (Yang et al., Findings 2024)
ACL
- Xianjun Yang, Liangming Pan, Xuandong Zhao, Haifeng Chen, Linda Ruth Petzold, William Yang Wang, and Wei Cheng. 2024. A Survey on Detection of LLMs-Generated Content. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 9786–9805, Miami, Florida, USA. Association for Computational Linguistics.