@inproceedings{kunnas-etal-2026-educational,
title = "Is Educational Content Enough? The Linguistic and Thematic Effects of Educational Filtering",
author = "Kunnas, Urho and
Laippala, Veronika and
Pyysalo, Sampo and
Galica, Tomasz",
editor = "Ortiz Suarez, Pedro and
Burchell, Laurie and
Ljube{\v{s}}i{\'c}, Nikola and
Scherrer, Yves and
Laippala, Veronika and
Dini{\'c}, Vuk and
Vaughan, Thom",
booktitle = "Proceedings of the 13th Web-as-Corpus Workshop",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wac-1.11/",
pages = "115--130",
ISBN = "979-8-89176-481-1",
abstract = "Corpus filtering strategies have evolved steadily, leading to increasingly effective data selection techniques that improve downstream LLM performance. However, while most filters focus on knowledge-rich and educational content, their effect on the linguistic and thematic composition of the documents in pre-training corpora remains underexplored. In this paper, we investigate how educational filtering affects both linguistic and thematic content by combining Propella-1 educational value annotations and the FinePDFs-Edu filter with the MultiCORE register (genre) framework. Focusing on the Finnish subset of the HPLT 4.0 corpus, we examine how educational filtering changes the distribution of registers and how educational value relates to register categories. By studying machine translated documents with high educational value we show that educational filters focus on content over textual quality. We further analyze changes in the thematic composition using Propella-1 business sector labels, examining whether educational filtering shifts the representation of different content domains. Finally, to assess the impact of filtering, we trained three small language models and evaluated their downstream performance on benchmark tasks. We find that educational filters disagree in their assessment of document educational value, show similar preferences for linguistic registers, but differ in the thematic content they select, resulting in different downstream performance across task types."
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<abstract>Corpus filtering strategies have evolved steadily, leading to increasingly effective data selection techniques that improve downstream LLM performance. However, while most filters focus on knowledge-rich and educational content, their effect on the linguistic and thematic composition of the documents in pre-training corpora remains underexplored. In this paper, we investigate how educational filtering affects both linguistic and thematic content by combining Propella-1 educational value annotations and the FinePDFs-Edu filter with the MultiCORE register (genre) framework. Focusing on the Finnish subset of the HPLT 4.0 corpus, we examine how educational filtering changes the distribution of registers and how educational value relates to register categories. By studying machine translated documents with high educational value we show that educational filters focus on content over textual quality. We further analyze changes in the thematic composition using Propella-1 business sector labels, examining whether educational filtering shifts the representation of different content domains. Finally, to assess the impact of filtering, we trained three small language models and evaluated their downstream performance on benchmark tasks. We find that educational filters disagree in their assessment of document educational value, show similar preferences for linguistic registers, but differ in the thematic content they select, resulting in different downstream performance across task types.</abstract>
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%0 Conference Proceedings
%T Is Educational Content Enough? The Linguistic and Thematic Effects of Educational Filtering
%A Kunnas, Urho
%A Laippala, Veronika
%A Pyysalo, Sampo
%A Galica, Tomasz
%Y Ortiz Suarez, Pedro
%Y Burchell, Laurie
%Y Ljubešić, Nikola
%Y Scherrer, Yves
%Y Laippala, Veronika
%Y Dinić, Vuk
%Y Vaughan, Thom
%S Proceedings of the 13th Web-as-Corpus Workshop
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-481-1
%F kunnas-etal-2026-educational
%X Corpus filtering strategies have evolved steadily, leading to increasingly effective data selection techniques that improve downstream LLM performance. However, while most filters focus on knowledge-rich and educational content, their effect on the linguistic and thematic composition of the documents in pre-training corpora remains underexplored. In this paper, we investigate how educational filtering affects both linguistic and thematic content by combining Propella-1 educational value annotations and the FinePDFs-Edu filter with the MultiCORE register (genre) framework. Focusing on the Finnish subset of the HPLT 4.0 corpus, we examine how educational filtering changes the distribution of registers and how educational value relates to register categories. By studying machine translated documents with high educational value we show that educational filters focus on content over textual quality. We further analyze changes in the thematic composition using Propella-1 business sector labels, examining whether educational filtering shifts the representation of different content domains. Finally, to assess the impact of filtering, we trained three small language models and evaluated their downstream performance on benchmark tasks. We find that educational filters disagree in their assessment of document educational value, show similar preferences for linguistic registers, but differ in the thematic content they select, resulting in different downstream performance across task types.
%U https://aclanthology.org/2026.wac-1.11/
%P 115-130
Markdown (Informal)
[Is Educational Content Enough? The Linguistic and Thematic Effects of Educational Filtering](https://aclanthology.org/2026.wac-1.11/) (Kunnas et al., WAC 2026)
ACL