Authorship verification (AV) is the task of determining whether two texts were written by the same author and has been studied extensively, predominantly for English data. In contrast, large-scale benchmarks and systematic evaluations for other languages remain scarce. We address this gap by introducing GerAV, a comprehensive benchmark for German AV comprising over 400k labeled text pairs. GerAV is built from Twitter and Reddit data, with the Reddit part further divided into in-domain and cross-domain message-based subsets, as well as a profile-based subset. This design enables controlled analysis of the effects of data source, topical domain, and text length. Using the provided training splits, we conduct a systematic evaluation of strong baselines and state-of-the-art models and find that our best approach, a fine-tuned large language model, outperforms recent baselines by up to 0.09 absolute F1 score and surpasses GPT-5 in a zero-shot setting by 0.08. We further observe a trade-off between specialization and generalization: models trained on specific data types perform best under matching conditions but generalize less well across data regimes, a limitation that can be mitigated by combining training sources. Overall, GerAV provides a challenging and versatile benchmark for advancing research on German and cross-domain AV.

The increasing capabilities of large language models (LLMs) have unlocked transformative potential for medical applications, but privacy constraints limit access to high-quality training data from electronic health records (EHRs). In response, we propose a framework to generate synthetic EHRs by instruction-tuning an LLM using descriptions of diagnosis codes. We show that this framework overcomes problems of prior approaches, such as diversity reduction and medical incoherence, while maintaining strong privacy protections. Utility was measured by training models to predict diagnosis codes for EHRs. Real data still has higher utility, but synthetic data approaches real data results with increasing dataset size. The differences in utility were most likely due to noise in the synthetic data. A user study involving medical professionals confirmed no significant loss in readability or medical coherence compared to the real HRs, even though inter-annotator agreement is low. These findings establish synthetic EHRs as a viable alternative for privacypreserving and scalable clinical NLP applications. We release our code on GitHub.