Due to strict privacy regulations, text corpora in non-English clinical contexts are scarce. Consequently, synthetic data generation using Large Language Models (LLMs) emerges as a promising strategy to address this data gap. To evaluate the ability of LLMs in generating synthetic data, we applied them to our novel German Medical Interview Questions Corpus (GerMedIQ), which consists of 4,524 unique, simulated question-response pairs in German. We augmented our corpus by prompting 18 different LLMs to generate responses to the same questions. Structural and semantic evaluations of the generated responses revealed that large-sized language models produced responses comparable to those provided by humans. Additionally, an LLM-as-a-judge study, combined with a human baseline experiment assessing response acceptability, demonstrated that human raters preferred the responses generated by Mistral (124B) over those produced by humans. Nonetheless, our findings indicate that using LLMs for data augmentation in non-English clinical contexts requires caution.

Pre-trained language models have significantly advanced natural language processing (NLP), especially with the introduction of BERT and its optimized version, RoBERTa. While initial research focused on English, single-language models can be advantageous compared to multilingual ones in terms of pre-training effort, overall resource efficiency or downstream task performance. Despite the growing popularity of prompt-based LLMs, more compute-efficient BERT-like models remain highly relevant. In this work, we present the first German single-language RoBERTa model, GottBERT, pre-trained exclusively on the German portion of the OSCAR dataset. Additionally, we investigated the impact of filtering the OSCAR corpus. GottBERT was pre-trained using fairseq and standard hyperparameters. We evaluated its performance on two Named Entity Recognition (NER) tasks (Conll 2003 and GermEval 2014) and three text classification tasks (GermEval 2018 fine and coarse, and 10kGNAD) against existing German BERT models and two multilingual models. Performance was measured using the F1 score and accuracy. The GottBERT base and large models showed competitive performance, with GottBERT leading among the base models in 4 of 6 tasks. Contrary to our expectation, the applied filtering did not significantly affect the results. To support the German NLP research community, we are releasing the GottBERT models under the MIT license.