CEAID: Benchmark of Multilingual Machine-Generated Text Detection Methods for Central European Languages
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Abstract (arXiv)
Machine-generated text detection, as an important task, is predominantly focused on English in research. This makes the existing detectors almost unusable for non-English languages, relying purely on cross-lingual transferability. There exist only a few works focused on any of Central European languages, leaving the transferability towards these languages rather unexplored. We fill this gap by providing the first benchmark of detection methods focused on this region, while also providing comparison of train-languages combinations to identify the best performing ones. We focus on multi-domain, multi-generator, and multilingual evaluation, pinpointing the differences of individual aspects, as well as adversarial robustness of detection methods. Supervised finetuned detectors in the Central European languages are found the most performant in these languages as well as the most resistant against obfuscation.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 26 references that are in the map. First posted on arXiv 30 Sep 2025.
Builds on
13 of 26 references.
References · 13
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- 2024Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Textcited by 263
- 2024RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectorscited by 163
- 2023MAGE: Machine-generated Text Detection in the Wildcited by 132
- 2023Testing of Detection Tools for AI-Generated Textcited by 90
- 2022Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russiancited by 29
- 2023IMGTB: A Framework for Machine-Generated Text Detection Benchmarkingcited by 12
- 2025Increasing the Robustness of the Fine-tuned Multilingual Machine-Generated Text Detectorscited by 8
- 2023Classification of Human- and AI-Generated Texts for English, French, German, and Spanishcited by 4
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Citing papers per year: 2026: 2.
2 papers.