German Text Simplification: Finetuning Large Language Models with
  Semi-Synthetic Data
Lars Klöser Mika Beele Jan-Niklas Schagen Bodo Kraft
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.