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There is a lack of research into capabilities of recent LLMs to generateconvincing text in languages other than English and into performance ofdetectors of machine-generated text in multilingual settings. This is alsoreflected in the available benchmarks which lack authentic texts in languagesother than English and predominantly cover older generators. To fill this gap,we introduce MULTITuDE, a novel benchmarking dataset for multilingualmachine-generated text detection comprising of 74,081 authentic andmachine-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 comparethe performance of zero-shot (statistical and black-box) and fine-tuneddetectors. Considering the multilinguality, we evaluate 1) how these detectorsgeneralize to unseen languages (linguistically similar as well as dissimilar)and unseen LLMs and 2) whether the detectors improve their performance whentrained on multiple languages.

MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark