Jindřich Helcl

• Oepen, Stephan; Arefyev, Nikolay; Aulamo, Mikko; Bañón, Marta; Buljan, Maja & Burchell, Laurie V. [Show all 29 contributors for this article] (2026). HPLT 3.0: Very Large-Scale Multilingual Resources for LLMs and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Models. In Piperidis,, Stelios; Bel, Núria; Heuvel, Henk van den; Ide, Nancy; Krek, Simon & Toral, Antonio (Ed.), Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026). European Language Resources Association. ISSN 9782493814494. p. 1409–1434. doi: 10.63317/25xbdofco9od. Full text in Research Archive Show summary

  We present an ongoing initiative to provide open, very large, high-quality, and richly annotated textual datasets for almost 200 languages. At 30 trillion tokens, this is likely the largest generally available multilingual collection of LLM pre-training data. These datasets are derived from web crawls from different sources and accompanied with a complete, open-source pipeline for document selection from web archives, text extraction from HTML, language identification for noisy texts, exact and near-deduplication, annotation with, among others, register labels, text quality estimates, and personally identifiable information; and final selection and filtering. We report on data quality probes through contrastive and analytical statistics, through manual inspection of samples for some 20 languages, and through end-to-end evaluation of various language model architectures trained on this data. For multilingual LLM evaluation, we provide a comprehensive collection of benchmarks for nine European languages, with special emphasis on natively created tasks, mechanisms to mitigate prompt sensitivity, and refined normalization and aggregation of scores. Additionally, we train and evaluate a family of 57 monolingual encoder–decoder models, as well as about 30 “smallish” monolingual GPT-like reference models. Besides the monolingual data and models, we also present a very large collection of parallel texts automatically mined from this data, together with a novel parallel corpus synthesized via machine translation.

• Fedorova, Mariia; Arefyev, Nikolay; Buljan, Maja; Helcl, Jindřich; Oepen, Stephan & Rønningstad, Egil [Show all 7 contributors for this article] (2026). OpenLID-v3: Improving the Precision of Closely Related Language Identification – An Experience Report. In Scherrer, Yves; Aepli, Noëmi; Blaschke, Verena; Jauhiainen, Tommi; Ljubešić, Nikola; Nakov, Preslav; Tiedemann, Jörg & Zampieri, Marcos (Ed.), Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects. Association for Computational Linguistics (ACL). ISSN 9798891763722. p. 275–292. doi: 10.18653/v1/2026.vardial-1.23. Full text in Research Archive Show summary

  Language identification (LID) is an essential step in building high-quality multilingual datasets from web data. Existing LID tools (such as OpenLID or GlotLID) often struggle to identify closely related languages and to distinguish valid natural language from noise, which contaminates language-specific subsets, especially for low-resource languages. In this work we extend the OpenLID classifier by adding more training data, merging problematic language variant clusters, and introducing a special label for marking noise. We call this extended system OpenLID-v3 and evaluate it against GlotLID on multiple benchmarks. During the development we focus on three groups of closely related languages (Bosnian, Croatian, and Serbian; Romance varieties of Northern Italy and Southern France; and Scandinavian languages) and contribute new evaluation datasets where existing ones are inadequate. We find that ensemble approaches improve precision but also substantially reduce coverage for low-resource languages.

• Burchell, Laurie; Gibert, Ona de; Arefev, Nikolay; Aulamo, Mikko; Bañón, Marta & Chen, Pinzhen [Show all 35 contributors for this article] (2025). An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT). In Che, Wanxiang; Nabende, Joyce; Shutova, Ekaterina & Pilehvar, Mohammad (Ed.), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics (ACL). ISSN 9798891762510. p. 17452–17485. doi: 10.18653/v1/2025.acl-long.854. Full text in Research Archive Show summary

  Training state-of-the-art large language models requires vast amounts of clean and diverse textual data. However, building suitable multilingual datasets remains a challenge. In this work, we present HPLT v2, a collection of high-quality multilingual monolingual and parallel corpora, extending prior work of the HPLT project. The monolingual portion of the data contains 8T tokens covering 193 languages, while the parallel data contains 380M sentence pairs covering 51 languages. We document the entire data pipeline and release the code to reproduce it. We provide extensive analysis of the quality and characteristics of our data. Finally, we evaluate the performance of language models and machine translation systems trained on HPLT v2, demonstrating its value.

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