The ability to paraphrase texts across different complexity levels is essential for creating accessible texts that can be tailored toward diverse reader groups. Thus, we introduce German4All, the first large-scale German dataset of aligned readability-controlled, paragraph-level paraphrases. It spans five readability levels and comprises over 25,000 samples. The dataset is automatically synthesized using GPT-4 and rigorously evaluated through both human and LLM-based judgments. Using German4All, we train an open-source, readability-controlled paraphrasing model that achieves state-of-the-art performance in German text simplification, enabling more nuanced and reader-specific adaptations. We opensource both the dataset and the model to encourage further research on multi-level paraphrasing
German4All -- A Dataset and Model for Readability-Controlled Paraphrasing in German
A large-scale German dataset and model for readability-controlled paraphrasing are introduced, achieving state-of-the-art performance in text simplification.
- Year
- 2025
- Venue
- arXiv 2025
- Authors
- 6
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2508.17973v2ARXIV-DEFAULT
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