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.