@inproceedings{ali-etal-2025-judging,
title = "Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models",
author = {Ali, Mehdi and
Brack, Manuel and
L{\"u}bbering, Max and
Wendt, Elias and
Khan, Abbas Goher and
Rutmann, Richard and
Jude, Alex and
Kraus, Maurice and
Weber, Alexander Arno and
Stollenwerk, Felix and
Kacz{\'e}r, David and
Mai, Florian and
Flek, Lucie and
Sifa, Rafet and
Flores-Herr, Nicolas and
Koehler, Joachim and
Schramowski, Patrick and
Fromm, Michael and
Kersting, Kristian},
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.449/",
pages = "8870--8909",
ISBN = "979-8-89176-332-6",
abstract = "High-quality multilingual training data is essential for effectively pretraining large language models (LLMs). Yet, the availability of suitable open-source multilingual datasets remains limited. Existing state-of-the-art datasets mostly rely on heuristic filtering methods, restricting both their cross-lingual transferability and scalability. Here, we introduce JQL, a systematic approach that efficiently curates diverse and high-quality multilingual data at scale while significantly reducing computational demands. JQL distills LLMs' annotation capabilities into lightweight annotators based on pretrained multilingual embeddings. These models exhibit robust multilingual and cross-lingual performance, even for languages and scripts unseen during training. Evaluated empirically across 35 languages, the resulting annotation pipeline substantially outperforms current heuristic filtering methods like Fineweb2. JQL notably enhances downstream model training quality and increases data retention rates. Our research provides practical insights and valuable resources for multilingual data curation, raising the standards of multilingual dataset development."
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<abstract>High-quality multilingual training data is essential for effectively pretraining large language models (LLMs). Yet, the availability of suitable open-source multilingual datasets remains limited. Existing state-of-the-art datasets mostly rely on heuristic filtering methods, restricting both their cross-lingual transferability and scalability. Here, we introduce JQL, a systematic approach that efficiently curates diverse and high-quality multilingual data at scale while significantly reducing computational demands. JQL distills LLMs’ annotation capabilities into lightweight annotators based on pretrained multilingual embeddings. These models exhibit robust multilingual and cross-lingual performance, even for languages and scripts unseen during training. Evaluated empirically across 35 languages, the resulting annotation pipeline substantially outperforms current heuristic filtering methods like Fineweb2. JQL notably enhances downstream model training quality and increases data retention rates. Our research provides practical insights and valuable resources for multilingual data curation, raising the standards of multilingual dataset development.</abstract>
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%0 Conference Proceedings
%T Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models
%A Ali, Mehdi
%A Brack, Manuel
%A Lübbering, Max
%A Wendt, Elias
%A Khan, Abbas Goher
%A Rutmann, Richard
%A Jude, Alex
%A Kraus, Maurice
%A Weber, Alexander Arno
%A Stollenwerk, Felix
%A Kaczér, David
%A Mai, Florian
%A Flek, Lucie
%A Sifa, Rafet
%A Flores-Herr, Nicolas
%A Koehler, Joachim
%A Schramowski, Patrick
%A Fromm, Michael
%A Kersting, Kristian
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F ali-etal-2025-judging
%X High-quality multilingual training data is essential for effectively pretraining large language models (LLMs). Yet, the availability of suitable open-source multilingual datasets remains limited. Existing state-of-the-art datasets mostly rely on heuristic filtering methods, restricting both their cross-lingual transferability and scalability. Here, we introduce JQL, a systematic approach that efficiently curates diverse and high-quality multilingual data at scale while significantly reducing computational demands. JQL distills LLMs’ annotation capabilities into lightweight annotators based on pretrained multilingual embeddings. These models exhibit robust multilingual and cross-lingual performance, even for languages and scripts unseen during training. Evaluated empirically across 35 languages, the resulting annotation pipeline substantially outperforms current heuristic filtering methods like Fineweb2. JQL notably enhances downstream model training quality and increases data retention rates. Our research provides practical insights and valuable resources for multilingual data curation, raising the standards of multilingual dataset development.
%U https://aclanthology.org/2025.emnlp-main.449/
%P 8870-8909
Markdown (Informal)
[Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models](https://aclanthology.org/2025.emnlp-main.449/) (Ali et al., EMNLP 2025)
ACL
- Mehdi Ali, Manuel Brack, Max Lübbering, Elias Wendt, Abbas Goher Khan, Richard Rutmann, Alex Jude, Maurice Kraus, Alexander Arno Weber, Felix Stollenwerk, David Kaczér, Florian Mai, Lucie Flek, Rafet Sifa, Nicolas Flores-Herr, Joachim Koehler, Patrick Schramowski, Michael Fromm, and Kristian Kersting. 2025. Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 8870–8909, Suzhou, China. Association for Computational Linguistics.