Mariia Fedorova

• Oepen, Stephan; Arefyev, Nikolay; Aulamo, Mikko; Bañón, Marta; Buljan, Maja & Burchell, Laurie V. [Vis alle 29 forfattere av denne artikkelen] (2026). HPLT 3.0: Very Large-Scale Multilingual Resources for LLMs and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Models. I Piperidis,, Stelios; Bel, Núria; Heuvel, Henk van den; Ide, Nancy; Krek, Simon & Toral, Antonio (Red.), Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026). European Language Resources Association. ISSN 9782493814494. s. 1409–1434. doi: 10.63317/25xbdofco9od. Fulltekst i vitenarkiv Vis sammendrag

  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 [Vis alle 7 forfattere av denne artikkelen] (2026). OpenLID-v3: Improving the Precision of Closely Related Language Identification – An Experience Report. I Scherrer, Yves; Aepli, Noëmi; Blaschke, Verena; Jauhiainen, Tommi; Ljubešić, Nikola; Nakov, Preslav; Tiedemann, Jörg & Zampieri, Marcos (Red.), Proceedings of the 13th Workshop on NLP for Similar Languages, Varieties and Dialects. Association for Computational Linguistics (ACL). ISSN 9798891763722. s. 275–292. doi: 10.18653/v1/2026.vardial-1.23. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Fedorova, Mariia; Kutuzov, Andrey & Umarova, Khonzoda (2026). DHPLT: large-scale multilingual diachronic corpora and word representations for semantic change modelling. I Tahmasebi, Nina; Cassotti, Pierluigi; Montariol, Syrielle; Kutuzov, Andrey; Huebscher, Netta; Spaziani, Elena & Baes, Naomi (Red.), The Proceedings for the 6th International Workshop on Computational Approaches to Language Change (LChange’26). Association for Computational Linguistics (ACL). ISSN 9798891763623. s. 87–96. doi: 10.18653/v1/2026.lchange-1.7. Fulltekst i vitenarkiv Vis sammendrag

  In this resource paper, we present DHPLT, an open collection of diachronic corpora in 41 diverse languages. DHPLT is based on the web-crawled HPLT datasets; we use web crawl timestamps as the approximate signal of document creation time. The collection covers three time periods: 2011-2015, 2020-2021 and 2024-present (1 million documents per time period for each language). We additionally provide pre-computed word type and token embeddings and lexical substitutions for our chosen target words, while at the same time leaving it open for the other researchers to come up with their own target words using the same datasets. DHPLT aims at filling in the current lack of multilingual diachronic corpora for semantic change modelling (beyond a dozen of high-resource languages). It opens the way for a variety of new experimental setups in this field.

• Fedorova, Mariia; Kutuzov, Andrey; Periti, Francesco & Scherrer, Yves (2025). Explaining novel senses using definition generation with open language models. I Christodoulopoulos, Christos; Chakraborty, Tanmoy; Rose, Carolyn & Peng, Violet (Red.), Findings of the Association for Computational Linguistics: EMNLP 2025. Association for Computational Linguistics (ACL). ISSN 9798891763357. s. 22294–22302. doi: 10.18653/v1/2025.findings-emnlp.1214. Fulltekst i vitenarkiv Vis sammendrag

  We apply definition generators based on open-weights large language models to the task of creating explanations of novel senses, taking target word usages as an input. To this end, we employ the datasets from the AXOLOTL’24 shared task on explainable semantic change modeling, which features Finnish, Russian and German languages. We fine-tune and provide publicly the open-source models performing higher than the best submissions of the aforementioned shared task, which employed closed proprietary LLMs. In addition, we find that encoder-decoder definition generators perform on par with their decoder-only counterparts.

• Fedorova, Mariia; Frydenberg, Jonas Sebulon; Handford, Victoria; Langø, Victoria Ovedie Chruickshank; Willoch, Solveig Helene & Midtgaard, Marthe Løken [Vis alle 9 forfattere av denne artikkelen] (2025). Multi-label Scandinavian Language Identification (SLIDE). I Holdt, Špela Arhar; Ilinykh, Nikolai; Scalvini, Barbara; Bruton, Micaella; Debess, Iben Nyholm & Tudor, Crina Madalina (Red.), Proceedings of the Third Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2025). Tartu Ülikool Kirjastus. ISSN 9789908531212. s. 179–189. Fulltekst i vitenarkiv Vis sammendrag

  Identifying closely related languages at sentence level is difficult, in particular because it is often impossible to assign a sentence to a single language. In this paper, we focus on multi-label sentence-level Scandinavian language identification (LID) for Danish, Norwegian Bokmål, Norwegian Nynorsk, and Swedish. We present the Scandinavian Language Identification and Evaluation, SLIDE, a manually curated multi-label evaluation dataset and a suite of LID models with varying speed–accuracy tradeoffs. We demonstrate that the ability to identify multiple languages simultaneously is necessary for any accurate LID method, and present a novel approach to training such multi-label LID models.

• Burchell, Laurie; Gibert, Ona de; Arefev, Nikolay; Aulamo, Mikko; Bañón, Marta & Chen, Pinzhen [Vis alle 35 forfattere av denne artikkelen] (2025). An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT). I Che, Wanxiang; Nabende, Joyce; Shutova, Ekaterina & Pilehvar, Mohammad (Red.), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics (ACL). ISSN 9798891762510. s. 17452–17485. doi: 10.18653/v1/2025.acl-long.854. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Fedorova, Mariia; Mickus, Timothee; Partanen, Niko; Siewert, Janine; Spaziani, Elena & Kutuzov, Andrei (2024). AXOLOTL’24 Shared Task on Multilingual Explainable Semantic Change Modeling, Proceedings of the 5th Workshop on Computational Approaches to Historical Language Change. Association for Computational Linguistics (ACL). ISSN 9798891761384. s. 72–91. Fulltekst i vitenarkiv Vis sammendrag

  This paper describes the organization and findings of AXOLOTL’24, the first multilingual explainable semantic change modeling shared task. We present new sense-annotated diachronic semantic change datasets for Finnish and Russian which were employed in the shared task, along with a surprise test-only German dataset borrowed from an existing source. The setup of AXOLOTL’24 is new to the semantic change modeling field, and involves subtasks of identifying unknown (novel) senses and providing dictionary-like definitions to these senses. The methods of the winning teams are described and compared, thus paving a path towards explainability in computational approaches to historical change of meaning.

• Fedorova, Mariia; Kutuzov, Andrei & Scherrer, Yves (2024). Definition generation for lexical semantic change detection, Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics (ACL). ISSN 9798891760998. s. 5712–5724. Fulltekst i vitenarkiv Vis sammendrag

  We use contextualized word definitions generated by large language models as semantic representations in the task of diachronic lexical semantic change detection (LSCD). In short, generated definitions are used as ‘senses’, and the change score of a target word is retrieved by comparing their distributions in two time periods under comparison. On the material of five datasets and three languages, we show that generated definitions are indeed specific and general enough to convey a signal sufficient to rank sets of words by the degree of their semantic change over time. Our approach is on par with or outperforms prior non-supervised sense-based LSCD methods. At the same time, it preserves interpretability and allows to inspect the reasons behind a specific shift in terms of discrete definitions-as-senses. This is another step in the direction of explainable semantic change modeling.

• Fedorova, Mariia; Kutuzov, Andrei; Arefev, Nikolay & Schlechtweg, Dominik (2024). Enriching Word Usage Graphs with Cluster Definitions. I Calzolari, Nicoletta; Kan, Min-Yen; Hoste, Veronique; Lenci, Alessandro; Sakti, Sakriani & Xue, Nianwen (Red.), Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). European Language Resources Association. ISSN 9782493814104. s. 6189–6198. Fulltekst i vitenarkiv Vis sammendrag

  We present a dataset of word usage graphs (WUGs), where the existing WUGs for multiple languages are enriched with cluster labels functioning as sense definitions. They are generated from scratch by fine-tuned encoder-decoder language models. The conducted human evaluation has shown that these definitions match the existing clusters in WUGs better than the definitions chosen from WordNet by two baseline systems. At the same time, the method is straightforward to use and easy to extend to new languages. The resulting enriched datasets can be extremely helpful for moving on to explainable semantic change modeling.

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