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

arXiv: https://arxiv.org/abs/2508.17973 · HTML: https://mlsanity.com/paper/2508.17973/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 25 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Subjective Assessment of Text Complexity: A Dataset for German Language](https://mlsanity.com/paper/1904.07733/) (2019)
- [Klexikon: A German Dataset for Joint Summarization and Simplification](https://mlsanity.com/paper/2201.07198/) (2022)
- [Language Models for German Text Simplification: Overcoming Parallel Data Scarcity through Style-specific Pre-training](https://mlsanity.com/paper/2305.12908/) (2023)
- [From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation](https://mlsanity.com/paper/2406.03030/) (2024)
- [DEPLAIN: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification](https://mlsanity.com/paper/2305.18939/) (2023)
- [InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification](https://mlsanity.com/paper/2401.16475/) (2024)
- [Analysing Zero-Shot Readability-Controlled Sentence Simplification](https://mlsanity.com/paper/2409.20246/) (2024)
- [Learning to Paraphrase Sentences to Different Complexity Levels](https://mlsanity.com/paper/2308.02226/) (2023)
- [A New Aligned Simple German Corpus](https://mlsanity.com/paper/2209.01106/) (2022)
- [LL"aMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratch](https://mlsanity.com/paper/2411.11171/) (2024)
- [Alignment Drift in CEFR-prompted LLMs for Interactive Spanish Tutoring](https://mlsanity.com/paper/2505.08351/) (2025)
- [German Text Simplification: Finetuning Large Language Models with Semi-Synthetic Data](https://mlsanity.com/paper/2402.10675/) (2024)
- [Evaluation Under Imperfect Benchmarks and Ratings: A Case Study in Text Simplification](https://mlsanity.com/paper/2504.09394/) (2025)
- [Data and Approaches for German Text simplification -- towards an Accessibility-enhanced Communication](https://mlsanity.com/paper/2312.09966/) (2023)
- [Images Speak Volumes: User-Centric Assessment of Image Generation for Accessible Communication](https://mlsanity.com/paper/2410.03430/) (2024)

## Built on by (most cited first, 2 of 2)

- [Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering](https://mlsanity.com/paper/2605.26620/) (2026)
- [Right at My Level: A Unified Multilingual Framework for Proficiency-Aware Text Simplification](https://mlsanity.com/paper/2604.05302/) (2026)

## Abstract (arXiv)

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