More Aligned, Less Diverse? Analyzing the Grammar and Lexicon of Two Generations of LLMs
Adrián Gude Universidad de La Coruña, Roi Santos-Rios, Francis Bond Palacký University Olomouc, Dan Flickinger, Carlos Gómez-Rodríguez Universidade da Coruña, Olga Zamaraeva Universidad de La Coruña
This study contributes to a growing line of research in comparing LLM-generated texts with human-authored text, in this case, English news text. We focus in particular on the evaluation of syntactic properties through formal grammar frameworks. Our analysis compares two generations of LLMs in the context of two human-authored English news datasets from two different years. Employing the Head-Driven Phrase Structure Grammar (HPSG) formalism, we investigate the distributions of syntactic structures and lexical types of AI-generated texts and contrast them with the corresponding distributions in the human-authored New Your Times (NYT) articles. We use diversity metrics from ecology and information theory to quantify variation in grammatical constructions and lexical types. Our results show that, while English news text has changed little in the given time frame, newer, instruction-tuned LLMs display reduced syntactic and, especially, lexical diversity compared to older, non-instruction-tuned models. These findings point to future work in studying effects of instruction tuning, which, while enhancing coherence and adherence to prompts, may narrow the expressive range of model output.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表