# German Text Simplification: Finetuning Large Language Models with Semi-Synthetic Data

arXiv: https://arxiv.org/abs/2402.10675 · HTML: https://mlsanity.com/paper/2402.10675/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Placed in

- Synthetic data and self-improvement for LLMs / [LLM synthetic data generation](https://mlsanity.com/synth/llm-synthetic-data-generation/)

## Builds on (most cited first, 8 of 8)

- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models](https://mlsanity.com/paper/2112.06598/) (2021)
- [BLEU is Not Suitable for the Evaluation of Text Simplification](https://mlsanity.com/paper/1810.05995/) (2018)
- [Contrastive Search Is What You Need For Neural Text Generation](https://mlsanity.com/paper/2210.14140/) (2022)
- [LENS: A Learnable Evaluation Metric for Text Simplification](https://mlsanity.com/paper/2212.09739/) (2022)
- [Language Models for German Text Simplification: Overcoming Parallel Data Scarcity through Style-specific Pre-training](https://mlsanity.com/paper/2305.12908/) (2023)
- [A New Aligned Simple German Corpus](https://mlsanity.com/paper/2209.01106/) (2022)
- [Using ChatGPT as a CAT tool in Easy Language translation](https://mlsanity.com/paper/2308.11563/) (2023)

## Built on by (most cited first, 4 of 4)

- [German4All -- A Dataset and Model for Readability-Controlled Paraphrasing in German](https://mlsanity.com/paper/2508.17973/) (2025)
- [A Typology of Synthetic Datasets for Dialogue Processing in Clinical Contexts](https://mlsanity.com/paper/2505.03025/) (2025)
- [Evaluating the Effectiveness of Direct Preference Optimization for Personalizing German Automatic Text Simplifications for Persons with Intellectual Disabilities](https://mlsanity.com/paper/2507.01479/) (2025)
- [Profiling German Text Simplification with Interpretable Model-Fingerprints](https://mlsanity.com/paper/2601.13050/) (2026)

## Abstract (arXiv)

This study pioneers the use of synthetically generated data for training generative models in document-level text simplification of German texts. We demonstrate the effectiveness of our approach with real-world online texts. Addressing the challenge of data scarcity in language simplification, we crawled professionally simplified German texts and synthesized a corpus using GPT-4. We finetune Large Language Models with up to 13 billion parameters on this data and evaluate their performance. This paper employs various methodologies for evaluation and demonstrates the limitations of currently used rule-based metrics. Both automatic and manual evaluations reveal that our models can significantly simplify real-world online texts, indicating the potential of synthetic data in improving text simplification.