MULTITuDE is a dataset for multilingual machine-generated text detection benchmark, described in the EMNLP 2023 conference paper. It consists of 7992 human-written news texts in 11 languages subsampled from MassiveSumm, accompanied by 66089 texts generated by 8 large language models (by using headlines of news articles). The creation process and scripts for replication/extension are located in a GitHub repository. If you use this dataset in any publication, project, tool or in any other form, please, cite the paper. Fields The dataset has the following fields: 'text' - a text sample, 'label' - 0 for human-written text, 1 for machine-generated text, 'multi_label' - a string representing a large language model that generated the text or the string "human" representing a human-written text, 'split' - a string identifying train or test split of the dataset for the purpose of training and evaluation respectively, 'language' - the ISO 639-1 language code identifying the language of the given text, 'length' - word count of the given text, 'source' - a string identifying the source dataset / news medium of the given text. Statistics (the number of samples) Splits: train - 44786 test - 29295 Binary labels: 0 - 7992 1 - 66089 Multiclass labels: gpt-3.5-turbo - 8300 gpt-4 - 8300 text-davinci-003 - 8297 alpaca-lora-30b - 8290 vicuna-13b - 8287 opt-66b - 8229 llama-65b - 8229 opt-iml-max-1.3b - 8157 human - 7992 Languages: English (en) - 29460 (train + test) Spanish (es) - 11586 (train + test) Russian (ru) - 11578 (train + test) Dutch (nl) - 2695 (test) Catalan (ca) - 2691 (test) Czech (cs) - 2689 (test) German (de) - 2685 (test) Chinese (zh) - 2683 (test) Portuguese (pt) - 2673 (test) Arabic (ar) - 2673 (test) Ukrainian (uk) - 2668 (test)