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        <title>Is a Document Educational or Just Wikipedia-Style? — Pitfalls of Classifier-Based Quality Filtering</title>
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        <namePart type="given">Mateusz</namePart>
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            <namePart type="given">Maria</namePart>
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            <namePart type="given">Viviane</namePart>
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            <namePart type="given">Jiajun</namePart>
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    <abstract>Classifier-based Quality Filtering has recently emerged as a fundamental technique in constructing pre-training corpora. The ability to deploy a single model that can replace or supplement a set of heuristics has proven effective across numerous Large Language Models. In this work, we expose a critical vulnerability in this approach by demonstrating how a straightforward Wikipedia-style reformatting operation can substantially alter a model’s quality assessment and enable low-quality content to surpass filtering thresholds. Our analysis reveals that the FineWeb-Edu CQF model would reverse its filtering decision for approximately 7% of evaluated documents, thereby admitting content into the pre-training corpus that would otherwise have been excluded.</abstract>
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%0 Conference Proceedings
%T Is a Document Educational or Just Wikipedia-Style? — Pitfalls of Classifier-Based Quality Filtering
%A Klimaszewski, Mateusz
%A Andruszkiewicz, Piotr
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-391-3
%F klimaszewski-andruszkiewicz-2026-document
%X Classifier-based Quality Filtering has recently emerged as a fundamental technique in constructing pre-training corpora. The ability to deploy a single model that can replace or supplement a set of heuristics has proven effective across numerous Large Language Models. In this work, we expose a critical vulnerability in this approach by demonstrating how a straightforward Wikipedia-style reformatting operation can substantially alter a model’s quality assessment and enable low-quality content to surpass filtering thresholds. Our analysis reveals that the FineWeb-Edu CQF model would reverse its filtering decision for approximately 7% of evaluated documents, thereby admitting content into the pre-training corpus that would otherwise have been excluded.
%R 10.18653/v1/2026.acl-short.10
%U https://aclanthology.org/2026.acl-short.10/
%U https://doi.org/10.18653/v1/2026.acl-short.10
%P 99-108

Markdown (Informal)

[Is a Document Educational or Just Wikipedia-Style? — Pitfalls of Classifier-Based Quality Filtering](https://aclanthology.org/2026.acl-short.10/) (Klimaszewski & Andruszkiewicz, ACL 2026)

• Is a Document Educational or Just Wikipedia-Style? — Pitfalls of Classifier-Based Quality Filtering (Klimaszewski & Andruszkiewicz, ACL 2026)

ACL

• Mateusz Klimaszewski and Piotr Andruszkiewicz. 2026. Is a Document Educational or Just Wikipedia-Style? — Pitfalls of Classifier-Based Quality Filtering. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 99–108, San Diego, California, United States. Association for Computational Linguistics.