arXiv:2605.23651v1 Announce Type: new Abstract: 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
The accelerating deployment and integration of Large Language Models necessitate a deeper understanding of their linguistic output beyond mere task performance, as their human-likeness impacts user perception and trust.
A strategic reader should care because the linguistic human-likeness of LLMs directly influences their adoption, ethical implications, and the effectiveness of human-AI collaboration.
This framework shifts the evaluation of LLMs from purely functional metrics to include nuanced linguistic quality, potentially influencing future model development and fine-tuning strategies.
- · Linguists
- · NLP researchers focused on human-like generation
- · Companies developing ethical AI
- · AI product designers
- · LLMs with superficial evaluation metrics
- · Platforms prioritizing speed over linguistic quality
Increased focus on linguistic complexity and context-awareness in LLM design.
Development of new datasets and benchmarks specifically for evaluating linguistic human-likeness across various registers.
More sophisticated and less detectable AI-generated content, potentially complicating issues of authenticity and disinformation.
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