While factual correctness and task-performance have received much attention by Large Language Model (LLM) research, the fundamental question of how human-like generated texts are on a linguistic level has been underexplored. From a corpus-linguistic perspective, language production is inherently context-dependent. Distinct communicative contexts give rise to differences in frequencies and co-occurrence patterns of linguistic features that shape registers of language use. A text failing to adhere to these patterns can be correct content-wise, but still may not appear natural to human readers. In this work, we propose a context-aware evaluation framework in which human-likeness is measured as a two-sample problem between the linguistic feature distribution of a human reference corpus for a given register and a corresponding LLM-generated corpus. We implement this framework using the Maximum Mean Discrepancy (MMD) measure and the 67 lexico-grammatical features introduced by Biber, which are commonly applied in corpus linguistics.

In our experiments, we compare seven instruction-tuned, open-source models and look at five English-language datasets spanning distinct registers. While across all tested setups, LLMs deviate from the human baseline, which models are closest to human language depends on the register and is not dictated by model size.

Previous work links Major Depressive Disorder (MDD) to linguistic and paralinguistic speech patterns, highlighting speech analysis as a potential diagnostic tool for remote monitoring or clinical decision support (CDS) systems. Recent attempts to build MDD diagnostic models primarily rely on the English (E-)DAIC corpus. Despite promising results, these works rarely evaluate cross-corpus generalization and rely on self-reported questionnaires, limiting their clinical utility. We introduce a new, clinically validated German corpus, EmpkinS-EKSpression, of semi-structured diagnostic interviews to test the reliability of spoken language-based measures of MDD. Our model achieves an F1-score of 0.86 on the EmpkinS-EKSpression dataset and 0.74 on E-DAIC via zero-shot cross-corpus transfer, suggesting that MDD detection models can generalize across corpora and languages despite differences in interview format and ground truth definition, supporting further development of CDS systems.

In this paper, we analyze how Large Language Models (LLMs) employ worldbuilding strategies, focusing on setting as one measurable dimension of storyworld construction. We compare 1,000 AI-generated stories per model in English and German with human-authored fiction from Project Gutenberg. Building on prior work, we operationalize setting through five types of narrative space: "action," "perceived," "visual," "descriptive" and "no space", identified using fine-tuned BERT classifiers for German and English. We generate narratives using GPT 4.1, LlaMA 3.3, Mistral 3.2, and Gemma 3 and compare their spatial distributions to a human-authored baseline. We find that human-authored texts predominantly employ "action space," grounding narratives in embodied character-environment interaction, whereas LLMs systematically overproduce "perceived space," emphasizing atmosphere and affect. This divergence remains stable across narrative time. Overall, our findings show that LLMs exhibit worldbuilding patterns that differ consistently from human-authored fiction in ways that are both model-specific and language-sensitive.

There has been limited exploration of how to effectively integrate domain knowledge into machine learning for medical tabular data. Traditional approaches often rely on non-generalizable processes tailored to specific datasets. In contrast, recent advances in deep learning for language and tabular data are leading the way toward more generalizable and scalable methods of domain knowledge inclusion. In this paper, we first explore the need for domain knowledge in medical tabular data, categorize types of medical domain knowledge, and discuss how each can be leveraged in tabular machine learning. We then outline strategies for integrating this knowledge at various stages of the machine learning pipeline. Finally, building on recent advances in tabular deep learning, we propose future research directions to support the integration of domain knowledge.