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.

本研究首次使用合成数据来训练德语文档级文本简化的生成模型，通过真实世界在线文本论证了我们方法的有效性。解决语言简化中数据稀缺的挑战，我们爬取了经过专业简化的德语文本并使用 GPT-4 合成了一个语料库。我们在这些数据上微调了多达 130 亿参数的大型语言模型，并使用多种方法对其性能进行评估。该论文运用了各种评估方法，并展示了当前使用的基于规则的度量指标的局限性。自动和手动评估均表明，我们的模型可以显著简化真实世界的在线文本，显示了合成数据在改进文本简化方面的潜力。