Papers by Joachim Koehler

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.
LLM Parameters for Math Across Languages: Shared or Separate? (2026.acl-srw)

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Challenge: Existing research on large language models (LLMs) has focused on performance or representational properties, but it remains unclear whether these differences reflect language-specific parameters or a shared mechanism.
Approach: They propose to localize and compare model parameters that support mathematical reasoning across languages.
Outcome: The proposed analysis shows that the model parameters in English and lower-resource languages exhibit partial cross-lingual overlap with systematic language-dependent differences.

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