@inproceedings{wu-etal-2026-detectrl,
title = "{D}etect{RL}-{X}: Towards Reliable Multilingual and Real-World {LLM}-Generated Text Detection",
author = "Wu, Junchao and
Liu, Yefeng and
Zhu, Chenyu and
Zhang, Hao and
Wu, Zeyu and
Shi, Tianqi and
Du, Yichao and
Wang, Longyue and
Luo, Weihua and
Su, Jinsong and
Wong, Derek F.",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1773/",
pages = "38247--38294",
ISBN = "979-8-89176-390-6",
abstract = "The effective detection and governance of Large Language Model (LLM) generated content has become increasingly critical due to the growing risk of misuse. Despite the impressive performance of existing detectors, their reliability and potential in multilingual, real-world scenarios remain largely underexplored.In this study, we introduce DetectRL-X, a comprehensive multilingual benchmark designed to evaluate advanced detectors across 8 dimensions. The benchmark encompasses 8 languages commonly used in commercial contexts and collects human-written texts from 6 domains highly susceptible to LLM misuse. To better aligned with real-world applications, We create LLM-generated texts using 4 popular commercial LLMs, and include typical AI-assisted writing operations such as polishing, expanding, and condensing to capture authentic usage patterns. Furthermore, we develop a multilingual framework for paraphrasing and perturbation attacks to simulate diverse human modifications and writing noise, enabling stress testing of detectors across languages.Experimental results on DetectRL-X reveal the strengths and limitations of current state-of-the-art detectors when applied to diverse linguistic resources. We further analyze how domains, generators, attack strategies, text length, and refinement operations influence performance in different languages, underscoring DetectRL-X as an effective benchmark for strengthening multilingual and language-specific detectors."
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%0 Conference Proceedings
%T DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection
%A Wu, Junchao
%A Liu, Yefeng
%A Zhu, Chenyu
%A Zhang, Hao
%A Wu, Zeyu
%A Shi, Tianqi
%A Du, Yichao
%A Wang, Longyue
%A Luo, Weihua
%A Su, Jinsong
%A Wong, Derek F.
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F wu-etal-2026-detectrl
%X The effective detection and governance of Large Language Model (LLM) generated content has become increasingly critical due to the growing risk of misuse. Despite the impressive performance of existing detectors, their reliability and potential in multilingual, real-world scenarios remain largely underexplored.In this study, we introduce DetectRL-X, a comprehensive multilingual benchmark designed to evaluate advanced detectors across 8 dimensions. The benchmark encompasses 8 languages commonly used in commercial contexts and collects human-written texts from 6 domains highly susceptible to LLM misuse. To better aligned with real-world applications, We create LLM-generated texts using 4 popular commercial LLMs, and include typical AI-assisted writing operations such as polishing, expanding, and condensing to capture authentic usage patterns. Furthermore, we develop a multilingual framework for paraphrasing and perturbation attacks to simulate diverse human modifications and writing noise, enabling stress testing of detectors across languages.Experimental results on DetectRL-X reveal the strengths and limitations of current state-of-the-art detectors when applied to diverse linguistic resources. We further analyze how domains, generators, attack strategies, text length, and refinement operations influence performance in different languages, underscoring DetectRL-X as an effective benchmark for strengthening multilingual and language-specific detectors.
%U https://aclanthology.org/2026.acl-long.1773/
%P 38247-38294
Markdown (Informal)
[DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection](https://aclanthology.org/2026.acl-long.1773/) (Wu et al., ACL 2026)
ACL
- Junchao Wu, Yefeng Liu, Chenyu Zhu, Hao Zhang, Zeyu Wu, Tianqi Shi, Yichao Du, Longyue Wang, Weihua Luo, Jinsong Su, and Derek F. Wong. 2026. DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 38247–38294, San Diego, California, United States. Association for Computational Linguistics.