@inproceedings{anschutz-etal-2025-german4all,
    title = "{G}erman4{A}ll {--} A Dataset and Model for Readability-Controlled Paraphrasing in {G}erman",
    author = {Ansch{\"u}tz, Miriam  and
      Pham, Thanh Mai  and
      Nasrallah, Eslam  and
      M{\"u}ller, Maximilian  and
      Craciun, Cristian-George  and
      Groh, Georg},
    editor = "Flek, Lucie  and
      Narayan, Shashi  and
      Phương, L{\^e} Hồng  and
      Pei, Jiahuan",
    booktitle = "Proceedings of the 18th International Natural Language Generation Conference",
    month = oct,
    year = "2025",
    address = "Hanoi, Vietnam",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-lei-gao-usc/2025.inlg-main.24/",
    pages = "390--407",
    abstract = "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 \textbf{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 open-source both the dataset and the model to encourage further research on multi-level paraphrasing."
}