--- title: "Who cites A New Massive Multilingual — 51 citing papers" description: "51 papers of the Elamur Research library cite «A New Massive Multilingual Dataset for High-Performance Language Technologies», found in their…" canonical: "https://research.elamur.ai/free/en/papers/gibert-2024-new-massive-multilingual-dataset-high/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/gibert-2024-new-massive-multilingual-dataset-high/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [A New Massive Multilingual Dataset for…](https://md.research.elamur.ai/free/en/papers/gibert-2024-new-massive-multilingual-dataset-high/) / cited by # Who cites «A New Massive Multilingual Dataset for High-Performance Language Technologies» 51 papers of our library cite it (of the 149,439 whose bibliographies we read) · 14 with a page here, the most cited first · [← back to «A New Massive Multilingual Dataset for High-Performance Language Technologies»](https://md.research.elamur.ai/free/en/papers/gibert-2024-new-massive-multilingual-dataset-high/) 1. [**EuroLLM: Multilingual Language Models for Europe arXiv:2409.16235**Pedro Henrique Martins, Patrick Fernandes, Joao Alves, et al.*2024 · 133 citations*](https://md.research.elamur.ai/free/en/papers/martins-2024-eurollm-multilingual-language-models-europe/) 2. [**FineWeb2: One Pipeline to Scale Them All — Adapting Pre-Training Data Processing to Every Language arXiv:2506.20920**Guilherme Penedo, Hynek Kydliček, Vinko Sabolčec, et al.*2025 · 99 citations*](https://md.research.elamur.ai/free/en/papers/penedo-2025-fineweb2-one-pipeline-scale-them/) 3. [**EuroLLM-9B: Technical Report arXiv:2506.04079**Pedro Henrique Martins, Joao Alves, Patrick Fernandes, et al.*2025 · 45 citations*](https://md.research.elamur.ai/free/en/papers/martins-2025-eurollm-9b-technical-report/) 4. [**EuroBERT: Scaling Multilingual Encoders for European Languages arXiv:2503.05500**Nicolas Boizard, Hippolyte Gisserot-Boukhlef, Duarte M. Alves, et al.*2025 · 34 citations*](https://md.research.elamur.ai/free/en/papers/boizard-2025-eurobert-scaling-multilingual-encoders-european/) 5. **[An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT)](https://md.research.elamur.ai/free/en/papers/burchell-2025-expanded-massive-multilingual-dataset-high/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Laurie Burchell, Ona de Gibert, Nikolay Arefyev, et al.*2025 · 23 citations* 6. **[Scaling Low-Resource MT via Synthetic Data Generation with LLMs](https://md.research.elamur.ai/free/en/papers/gibert-2025-scaling-low-resource-mt-synthetic/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ona de Gibert, Joseph Attieh, Teemu Vahtola, et al.*2025 · 15 citations* 7. **[NorEval: A Norwegian Language Understanding and Generation Evaluation Benchmark](https://md.research.elamur.ai/free/en/papers/mikhailov-2025-noreval-norwegian-language-understanding-generation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Vladislav Mikhailov, Tita Enstad, David Samuel, et al.*2025 · 7 citations* 8. **[Hala Technical Report Building Arabic-Centric Instruction & Translation Models at Scale](https://md.research.elamur.ai/free/en/papers/hammoud-2026-hala-technical-report-building-arabic/) [Workshop on NLP for Languages Using Arabic Script](https://md.research.elamur.ai/free/en/venues/abjadnlp/)**Hasan Abed Al Kader Hammoud, Mohamad Bilal Zbib, Bernard Ghanem*2026 · 5 citations* 9. [**The German Commons - 154 Billion Tokens of Openly Licensed Text for German Language Models arXiv:2510.13996**Lukas Gienapp, Christopher Schroder, Stefan Schweter, et al.*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/gienapp-2025-german-commons-154-billion-tokens/) 10. **[HPLT 3.0: Very Large-Scale Multilingual Resources for LLMs and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Models](https://md.research.elamur.ai/free/en/papers/oepen-2026-hplt-3-0-very-large/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Stephan Oepen, Nikolay Arefyev, Mikko Aulamo, et al.*2026 · 1 citation* 11. [**KletterMix: Climbing Toward High-Quality German Pretraining Data - The Full Report arXiv:2606.03773**Maurice Kraus, Ruben Harle, Sebastian Sztwiertnia, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/kraus-2026-klettermix-climbing-toward-high-quality/) 12. **[GPT-NL Public Corpus: A Permissively Licensed, Dutch-First Dataset for LLM Pre-training](https://md.research.elamur.ai/free/en/papers/oort-2026-gpt-nl-public-corpus-permissively/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Jesse J. Van Oort, Frank Brinkkemper, Erik de Graaf, et al.*2026* 13. **[Quality and Appropriateness of Large Text Datasets for Irish NLP](https://md.research.elamur.ai/free/en/papers/walsh-2026-quality-appropriateness-large-text-datasets/) [SIGUL Joint Workshop with ELE, EURALI, and DCLRL: Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages](https://md.research.elamur.ai/free/en/venues/sigul/)**Abigail Walsh, Mark Andrade, Jane Lauren Adkins, et al.*2026* 14. **[Small Can Be Beautiful in LLMs for SSH: a Case for Bulgarian](https://md.research.elamur.ai/free/en/papers/simov-2026-small-can-be-beautiful-llms/) [Shaping Multilingual, Multimodal AI for the Social Sciences and Humanities @ LREC](https://md.research.elamur.ai/free/en/venues/llms4ssh/)**Kiril Simov, Nikolay Paev, Petya Osenova, et al.*2026* 37 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/gibert-2024-new-massive-multilingual-dataset-high/) — A New Massive Multilingual Dataset for High-Performance Language Technologies