How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework
While factual correctness and task-performance have been in focus of Large Language Model (LLM) research for a long time, 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, with distinct communicative contexts giving rise to differences in frequencies and co-occurrence patterns of linguistic features. A text failing to adhere to these patterns can be content-wise correct, but still be unfavorable to human readers. In this work, we propose a context-aware evaluation framework in which human-likeness is assessed using a two-sample problem betwe...
| Authors | Björn Nieth, Marianna Gracheva, Michaela Mahlberg et al. |
|---|---|
| Primary category | cs.CL |
| Published | 22 May 2026 |
| Last updated at source | 1 September 2026 |
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