There is a lack of research into capabilities of recent LLMs to generate convincing text in languages other than English and into performance of detectors of machine-generated text in multilingual settings. This is also reflected in the available benchmarks which lack authentic texts in languages other than English and predominantly cover older generators. To fill this gap, we introduce MULTITuDE, a novel benchmarking dataset for multilingual machine-generated text detection comprising of 74,081 authentic and machine-generated texts in 11 languages (ar, ca, cs, de, en, es, nl, pt, ru, uk, and zh) generated by 8 multilingual LLMs. Using this benchmark, we compare the performance of zero-shot (statistical and black-box) and fine-tuned detectors. Considering the multilinguality, we evaluate 1) how these detectors generalize to unseen languages (linguistically similar as well as dissimilar) and unseen LLMs and 2) whether the detectors improve their performance when trained on multiple languages.
@article{arxiv.2310.13606,
title = {MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark},
author = {Dominik Macko and Robert Moro and Adaku Uchendu and Jason Samuel Lucas and Michiharu Yamashita and Matúš Pikuliak and Ivan Srba and Thai Le and Dongwon Lee and Jakub Simko and Maria Bielikova},
journal= {arXiv preprint arXiv:2310.13606},
year = {2024}
}