@inproceedings{galica-etal-2026-register,
title = "Register-based data selection for multilingual {LLM}s",
author = "Galica, Tomasz and
Myntti, Amanda and
Henriksson, Erik and
Vitiugin, Fedor and
Pyysalo, Sampo and
Laippala, Veronika",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.4/",
pages = "52--67",
ISBN = "979-8-89176-462-0",
abstract = "The development of high-quality multilingual models depends on well-curated multilingual datasets, which are typically obtained through filtering large web-crawled corpora. Such filtering approaches must be effective across a wide range of languages in order to improve model quality proportionally across the entire linguistic space. In recent years multiple Natural Language Processing (NLP) approaches for data processing and Large Language Model (LLM) training were developed. However, the most significant progress has been made for rich-resource languages such as English and Chinese, leaving multilingual methods largely underexplored. In this work, we present data selection strategies based on Internet registers (genres) and Web Document Score (WDS). We study 15 European languages utilizing HPLT 3.0 datasets. We train and evaluate four small generative language models per language, and compare how specific registers and WDS affect model training in separate and combined approaches. We use five common evaluation tasks covering models' linguistic, knowledge-based, and reasoning skills. We analyze the impact of the studied strategies on each language, their average performance, and show how combining register-based selection with WDS improves evaluation performance. These results indicate that both registers and WDS can generalize across multiple languages, offering a new path towards multilingual data filtering."
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<abstract>The development of high-quality multilingual models depends on well-curated multilingual datasets, which are typically obtained through filtering large web-crawled corpora. Such filtering approaches must be effective across a wide range of languages in order to improve model quality proportionally across the entire linguistic space. In recent years multiple Natural Language Processing (NLP) approaches for data processing and Large Language Model (LLM) training were developed. However, the most significant progress has been made for rich-resource languages such as English and Chinese, leaving multilingual methods largely underexplored. In this work, we present data selection strategies based on Internet registers (genres) and Web Document Score (WDS). We study 15 European languages utilizing HPLT 3.0 datasets. We train and evaluate four small generative language models per language, and compare how specific registers and WDS affect model training in separate and combined approaches. We use five common evaluation tasks covering models’ linguistic, knowledge-based, and reasoning skills. We analyze the impact of the studied strategies on each language, their average performance, and show how combining register-based selection with WDS improves evaluation performance. These results indicate that both registers and WDS can generalize across multiple languages, offering a new path towards multilingual data filtering.</abstract>
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%0 Conference Proceedings
%T Register-based data selection for multilingual LLMs
%A Galica, Tomasz
%A Myntti, Amanda
%A Henriksson, Erik
%A Vitiugin, Fedor
%A Pyysalo, Sampo
%A Laippala, Veronika
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F galica-etal-2026-register
%X The development of high-quality multilingual models depends on well-curated multilingual datasets, which are typically obtained through filtering large web-crawled corpora. Such filtering approaches must be effective across a wide range of languages in order to improve model quality proportionally across the entire linguistic space. In recent years multiple Natural Language Processing (NLP) approaches for data processing and Large Language Model (LLM) training were developed. However, the most significant progress has been made for rich-resource languages such as English and Chinese, leaving multilingual methods largely underexplored. In this work, we present data selection strategies based on Internet registers (genres) and Web Document Score (WDS). We study 15 European languages utilizing HPLT 3.0 datasets. We train and evaluate four small generative language models per language, and compare how specific registers and WDS affect model training in separate and combined approaches. We use five common evaluation tasks covering models’ linguistic, knowledge-based, and reasoning skills. We analyze the impact of the studied strategies on each language, their average performance, and show how combining register-based selection with WDS improves evaluation performance. These results indicate that both registers and WDS can generalize across multiple languages, offering a new path towards multilingual data filtering.
%U https://aclanthology.org/2026.findings-emnlp.4/
%P 52-67
Markdown (Informal)
[Register-based data selection for multilingual LLMs](https://aclanthology.org/2026.findings-emnlp.4/) (Galica et al., Findings 2026)
ACL
- Tomasz Galica, Amanda Myntti, Erik Henriksson, Fedor Vitiugin, Sampo Pyysalo, and Veronika Laippala. 2026. Register-based data selection for multilingual LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 52–67, Budapest, Hungary. Association for Computational Linguistics.