arXiv:2605.24850v1 Announce Type: new Abstract: Evaluating whether large language models (LLMs) capture the structure of natural language beyond local fluency remains an open challenge. Existing evaluation methods, largely based on task performance or short-context behavior, provide limited insight into the long-range statistical organization of generated text. We propose a complementary evaluation framework based on repeated subsequences. By analyzing their distribution across scales and relating it to higher-order R\'enyi entropies, we probe how texts reuse previously established structure u
The rapid advancement and widespread deployment of large language models necessitates more sophisticated evaluation methods to understand their true capabilities and limitations beyond superficial performance benchmarks.
This research provides a novel framework for assessing fundamental differences between AI-generated and human language, crucially identifying gaps in LLMs' ability to capture higher-order linguistic structures, which could dictate future AI development trajectories and applications.
The understanding of what constitutes 'human-like' language generation shifts from local fluency to long-range structural coherence, introducing new metrics for evaluating AI authenticity and sophistication.
- · AI ethicists
- · NLP researchers
- · Companies focused on human-in-the-loop AI
- · Developers relying solely on superficial LLM evaluation
- · Applications requiring deep natural language understanding without robust evalua
New evaluation benchmarks will emerge, challenging the perceived 'humanity' of current LLMs.
This could lead to a redirection of research efforts towards improving long-range coherence and statistical organization in generative AI.
Public and regulatory bodies might develop more nuanced understandings and demands for 'natural' or 'authentic' AI communication.
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Read at arXiv cs.CL