Multitude: Large-scale multilingual machine-generated text detection benchmark
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Dominik Macko, Robert Moro, Adaku Uchendu, Jason Lucas, Michiharu Yamashita, Matúš Pikuliak, Ivan Srba, Thai Le, Dongwon Lee, Jakub Simko, Maria Bielikova. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
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MULTITuDE consists of 74k machine-generated texts and 7k human-written texts across 11 languages.
“This dataset consists of 74k machine-generated texts and 7k human-written texts across 11 languages (Macko et al. 2023), including Arabic, Catalan, Chinese, Czech, Dutch, English, German, Portuguese, Russian, Spanish, and Ukrainian.”
From A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions (2025) · cites this paper as Macko et al. 2023
The Abstract (page unavailable) confirms that MULTITuDE covers the 11 listed languages, but says 74,081 is the total number of human-written and machine-generated texts—not the number of machine-generated texts alone. The human-text count of 7k is not established by the cited passages; Appendix B.1 (page unavailable) gives per-language sampling figures rather than that total. (LLM, conf=0.90)
Experimental — accuracy not yet measuredMULTITuDE's machine-generated texts are produced by eight generative models, including Alpaca-Lora, GPT-3.5-turbo, GPT-4, LLaMA, OPT, OPT-IML-Max, Text-Davinci-003, and Vicuna.
“The machinegenerated texts are produced by eight generative models, including Alpaca-Lora, GPT3.5-turbo, GPT-4, LLaMA, OPT, OPT-IML-Max, Text-Davinci-003, and Vicuna.”
From A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions (2025) · cites this paper as Macko et al. 2023
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Mady, M., Li, Y., Schuller, B. W., Şişman, B., & Reschke, J. (2026) · Research Square · doi:10.21203/rs.3.rs-10864156/v1
Kocagul, H. (2026) · Cankaya University Journal of Humanities and Social Sciences · doi:10.47777/cankujhss.1957142
KORKMAZ, Ş., Alkan, M., & Koçak, A. (2026) · Electronics · doi:10.3390/electronics15215108
- Authors
- Macko, D., Moro, R., Uchendu, A., Lucas, J., Yamashita, M., Pikuliak, M., Srba, I., Le, T., Lee, D., Simko, J., & Bielikova, M.
- Affiliations
- MIT Lincoln Laboratory (US)
- Pennsylvania State University (US)
- University of Mississippi (US)
- ORCID
- Venue
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Published
- 2023
- Pages
- 9960-9987
- OpenAlex
- W4389520338
- Cited by
- 38
- References
- 22
- Citations by year
- 2026: 13 · 2025: 17 · 2024: 8
- FWCI
- 6.73
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Science Foundation (1820609, 1934782, 2114824, 2131144)
- European Commission (101070093, 101073921)
- HORIZON EUROPE Framework Programme
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- AI-generated text detection, multilingual text detection, benchmark, large-scale benchmark, multilingual NLP
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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