@inproceedings{macko-kopal-2026-ceaid,
title = "{CEAID}: Benchmark of Multilingual Machine-Generated Text Detection Methods for {C}entral {E}uropean Languages",
author = "Macko, Dominik and
Kop{\'a}l, Jakub",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1458/",
doi = "10.18653/v1/2026.findings-acl.1458",
pages = "29177--29190",
ISBN = "979-8-89176-395-1",
abstract = "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."
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%0 Conference Proceedings
%T CEAID: Benchmark of Multilingual Machine-Generated Text Detection Methods for Central European Languages
%A Macko, Dominik
%A Kopál, Jakub
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F macko-kopal-2026-ceaid
%X 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.
%R 10.18653/v1/2026.findings-acl.1458
%U https://aclanthology.org/2026.findings-acl.1458/
%U https://doi.org/10.18653/v1/2026.findings-acl.1458
%P 29177-29190
Markdown (Informal)
[CEAID: Benchmark of Multilingual Machine-Generated Text Detection Methods for Central European Languages](https://aclanthology.org/2026.findings-acl.1458/) (Macko & Kopál, Findings 2026)
ACL