Papers by Maurice Weber

2 papers
Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models (2025.emnlp-main)

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Challenge: Existing open-source multilingual datasets rely on heuristic filtering methods restricting both their cross-lingual transferability and scalability.
Approach: They propose a systematic approach that curates diverse and high-quality multilingual data at scale while significantly reducing computational demands.
Outcome: Evaluated empirically across 35 languages, the proposed approach outperforms current heuristic filtering methods like Fineweb2 and improves model training quality and retention rates.
Multilingual Language Model Pretraining using Machine-translated Data (2025.emnlp-main)

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Challenge: Existing methods for collecting and filtering multilingual web data lead to most languages lagging behind English performance due to the Internet's English-centric nature.
Approach: They propose to translate a high-quality English web corpus into nine languages and pretrain a 1.3B-parameter model on it.
Outcome: The proposed model matches or outperforms multilingual LLMs of similar size across Non-English understanding and reasoning tasks despite being trained on an order of magnitude less data.

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