G erman4 A ll – A Dataset and Model for Readability-Controlled Paraphrasing in G erman (2025)

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 open-source both the dataset and the model to encourage further research on multi-level paraphrasing.

## introduction text simplification is typically approached as a standardized process, where an input text is simplified to a single, pre-defined complexity level-often determined by the model's training data. however, the audience for simplified language is highly diverse, including people with different reading proficiencies and backgrounds @xcite . to better address this diversity, some languages such as german and spanish differentiate between multiple simplification levels-for example, "plain language" and "easy-to-read" (de: leichte sprache, es: lectura fácil) @xcite . despite this, existing simplification systems for german generally treat simplification as a one-sizefits-all task targeting a single output level. in contrast, english nlp research has explored methods for adapting simplification to multiple complexity levels @xcite @xcite . yet, such finegrained control remains underexplored in non-english contexts due to a lack of suitable resources. the album was also nominated for best rock album at the 2019 grammy awards. however, greta van fleet's album "from the fires" was awarded. level 1: there was an award ceremony called the grammy awards. there is a prize there for the best rock music album. one album was suggested, but another album won. the album that won is called "from the fires". it is by greta van fleet. level 5: within the framework of the 2019 grammy awards, the aforementioned album was nominated in the category of "best rock album." however, the accolade was ultimately bestowed upon the album "from the fires" by greta van fleet, which succeeded in prevailing over its competitors. to address this gap, we introduce german4all, the first large-scale, multi-level paraphrasing corpus for german. the dataset provides paraphrases of source texts across five distinct complexity levels. these levels are defined by their respective target group and range from people with reading difficulties to academic experts. for this paper, we refer to text complexity, readability, and simplification level as similar concepts. the proposed dataset supports a range of tasks, including simplification, complexification, and readability-controlled paraphrasing. moreover, the aligned paraphrases can be used in various settings, including iterative simplification or readability assessment. overall, our contribution can be summarized as follows: figure 2 : overview of our dataset creation approach: we take paragraphs from wikipedia and use gpt-4 to paraphrase them into five different complexity levels (cls). then, we manually curate a test set wth 150 samples, while automatically evaluating the main dataset with an llm judge. german text simplification german exhibits different styles of simplified language. while simple/plain language (de: "einfache sprache") is a generally simpler version of standard german targeted to a broad audience, such as language learners (din-normenausschuss ergonomie, 2024), easy language (de: "leichte sprache") is specifically tailored towards people with mental disabilities and reading deficiencies. as such, the simplifications in easy language are way stronger with shorter sentences and more guidance for the reader, e.g., through line breaks after every sentence (din-normenausschuss ergonomie, 2025). many nlp resources exist for easy language @xcite @xcite , simple language @xcite , and other, more specific target groups like children @xcite . in addition, some works provide resources in multiple difficulty levels @xcite , most often following the common european framework of reference for languages (cefr) (council of europe, 2001). as such, @xcite were the first to explore readability-controlled simplification for german. despite these resources, there is a lack of large-scale and publicly available data for multilevel paraphrasing. we close this gap by providing a dataset with aligned paragraphs of different complexity levels. it not only supports simplification but also complexification to higher complexity levels. complexification can be especially interesting for language learners who want to gradually improve their language proficiency. moreover, input texts of various complexity can lead to a better generalization in simplification @xcite . ## methodology an overview of our data creation process is presented in figure 2 . the inputs in our dataset are paragraphs from wikipedia. for this, we selected the wikipedia dump from december 2022 in the cohere/wikipedia-22-12 dataset. this dataset contains wikipedia paragraphs with at least 100 characters in multiple languages and sorts them by the number of views of their corresponding page. from this data, we randomly selected 26,665 samples from the 3 million most popular german paragraphs as our main data and 150 samples from the 1 million most popular german paragraphs as a test set, while assuring that the two subsets have no overlap. in the full dataset, the upper quartile of words per paragraph is 76 words. hence, we excluded paragraphs with more than 80 words to ensure a diverse yet consistent paragraph length. ## dataset evaluation our synthesized data contains more than 25k samples with five complexity level paraphrases each, resulting in a dataset of more than 125k text pairs. due to this size, a human review of all samples would be infeasible. therefore, we investigated two different evaluation angles. first, we randomly selected samples from the corpus and performed a human evaluation on these samples. then, we extended our evaluation to the full corpus by using an llm-as-a-judge. ## model distillation the ultimate motivation for our dataset is to create a smaller model that can perform the task of readability-controlled paraphrasing without having to rely on large or expensive models. therefore, we have inserted and trained lora layers (hu et al., 2022) for a flan-t5-xl model @xcite that can be inferenced on consumer-sized graphic cards with 12gb of vram. we also experimented with different base models such as llama3.1 8b @xcite and the german-specific llämmlein 7b @xcite : the former displayed grammatical errors, while the latter struggled with instruction following. thus, we chose flan-t5 as base, even though it is already fairly old. we trained it using a random train:val-80:20 split of the german4all-main corpus. for every level, we took not only the original input but also the other complexity levels' paraphrases as inputs. this increased our training data by five and leads to a better model generalization as the model sees different styles and complexities as inputs. for our prompt design, we use the same german complexity level descriptions as for the dataset creation (appendix a). these descriptions are followed by the task "paraphrase the following text to level {level}. {input_text}" (translated here). ## conclusion in this paper, we present german4all, the first aligned multi-level paraphrasing dataset in german. with more than 25k samples and a flan-t5 model trained on this data, our work enables large-scale research on text simplification, paraphrasing, and readability assessment-going beyond what was previously possible for german.