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        <title>A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions</title>
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    <abstract>The remarkable ability of large language models (LLMs) to comprehend, interpret, and generate complex language has rapidly integrated LLM-generated text into various aspects of daily life, where users increasingly accept it. However, the growing reliance on LLMs underscores the urgent need for effective detection mechanisms to identify LLM-generated text. Such mechanisms are critical to mitigating misuse and safeguarding domains like artistic expression and social networks from potential negative consequences. LLM-generated text detection, conceptualized as a binary classification task, seeks to determine whether an LLM produced a given text. Recent advances in this field stem from innovations in watermarking techniques, statistics-based detectors, and neural-based detectors. Human-assisted methods also play a crucial role. In this survey, we consolidate recent research breakthroughs in this field, emphasizing the urgent need to strengthen detector research. Additionally, we review existing datasets, highlighting their limitations and developmental requirements. Furthermore, we examine various LLM-generated text detection paradigms, shedding light on challenges like out-of-distribution problems, potential attacks, real-world data issues, and ineffective evaluation frameworks. Finally, we outline intriguing directions for future research in LLM-generated text detection to advance responsible artificial intelligence. This survey aims to provide a clear and comprehensive introduction for newcomers while offering seasoned researchers valuable updates in the field.1</abstract>
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%0 Journal Article
%T A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
%A Wu, Junchao
%A Yang, Shu
%A Zhan, Runzhe
%A Yuan, Yulin
%A Chao, Lidia Sam
%A Wong, Derek Fai
%J Computational Linguistics
%D 2025
%8 March
%V 51
%N 1
%I MIT Press
%C Cambridge, MA
%F wu-etal-2025-survey
%X The remarkable ability of large language models (LLMs) to comprehend, interpret, and generate complex language has rapidly integrated LLM-generated text into various aspects of daily life, where users increasingly accept it. However, the growing reliance on LLMs underscores the urgent need for effective detection mechanisms to identify LLM-generated text. Such mechanisms are critical to mitigating misuse and safeguarding domains like artistic expression and social networks from potential negative consequences. LLM-generated text detection, conceptualized as a binary classification task, seeks to determine whether an LLM produced a given text. Recent advances in this field stem from innovations in watermarking techniques, statistics-based detectors, and neural-based detectors. Human-assisted methods also play a crucial role. In this survey, we consolidate recent research breakthroughs in this field, emphasizing the urgent need to strengthen detector research. Additionally, we review existing datasets, highlighting their limitations and developmental requirements. Furthermore, we examine various LLM-generated text detection paradigms, shedding light on challenges like out-of-distribution problems, potential attacks, real-world data issues, and ineffective evaluation frameworks. Finally, we outline intriguing directions for future research in LLM-generated text detection to advance responsible artificial intelligence. This survey aims to provide a clear and comprehensive introduction for newcomers while offering seasoned researchers valuable updates in the field.1
%R 10.1162/coli_a_00549
%U https://aclanthology.org/2025.cl-1.8/
%U https://doi.org/10.1162/coli_a_00549
%P 275-338

Markdown (Informal)

[A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions](https://aclanthology.org/2025.cl-1.8/) (Wu et al., CL 2025)

• A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions (Wu et al., CL 2025)

ACL

• Junchao Wu, Shu Yang, Runzhe Zhan, Yulin Yuan, Lidia Sam Chao, and Derek Fai Wong. 2025. A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions. Computational Linguistics, 51(1):275–338.