arXiv:2606.03773v1 Announce Type: new Abstract: High-quality pretraining data is a central ingredient in modern language models, but German-language resources remain far less developed than their English counterparts: they are often smaller, less carefully curated, weakly documented, and rarely validated through controlled training experiments. We introduce KletterMix, a high-quality German corpus for language model pretraining and annealing, designed as a reusable dataset artifact for the natural language processing and modeling community. KletterMix is built by translating a state-of-the-art
The increasing recognition of data quality as a bottleneck for language model performance, especially for non-English languages, drives the need for curated datasets like KletterMix.
This development is crucial for reducing dependency on English-centric AI infrastructure and fostering robust, locally-relevant AI capabilities for European nations.
The availability of a high-quality German pretraining corpus, validated through experiments, significantly lowers the barrier for developing advanced German language models.
- · German AI developers
- · European technology companies
- · NLP researchers
- · Sovereign AI initiatives
- · Platforms reliant on English-only data
- · AI models with weak multilingual capabilities
Improved performance and broader adoption of AI applications tailored for the German language.
Increased investment in high-quality data curation for other non-English European languages.
Reduced linguistic dependence on US-centric AI and accelerated development of independent European AI ecosystems.
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Read at arXiv cs.CL