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Linguistics

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DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection
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AI Knows When It's Being Watched: Functional Strategic Action and Contextual Register Modulation in Large Language Models
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WARDEN: Endangered Indigenous Language Transcription and Translation with 6 Hours of Training Data
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May 7, 2026Sofia Callejas, Nahuel Gomez, Catherine Pelachaud +2Humor Multilingual Automatic Speech Recognition
Linear Semantic Segmentation for Low-Resource Spoken Dialects
May 7, 2026Kirill Chirkunov, Younes Samih, Abed Alhakim Freihat +1Dialects Arabic
YEZE at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization via Heterogeneous Ensembling
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Visual Fingerprints for LLM Generation Comparison
May 7, 2026Amal Alnouri, Andreas Hinterreiter, Christina Humer +2Large Language Model Output Text Generation

May 15, 2026 · cs.CLJ/K move · Enter open · S save

DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection

Junchao Wu, Yefeng Liu, Chenyu Zhu, Hao Zhang+7

NLP2CT Lab, Department of Computer and Information Science, University of Macau · Alibaba Group · Xiamen University

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.

Machine-Generated Text Detection Multilingual Benchmark Multilingual Real-World

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