German4All -- A Dataset and Model for Readability-Controlled Paraphrasing in German

TL;DR

German4All is a large-scale dataset and model for generating paraphrases in German at different readability levels, facilitating accessible and tailored texts for diverse readers.

Contribution

Introduces the first large-scale German dataset of readability-controlled paraphrases and trains a state-of-the-art model for German text simplification.

Findings

Dataset spans five readability levels with over 25,000 samples.

Model achieves state-of-the-art performance in German text simplification.

Both dataset and model are open-sourced for further research.

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 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