用语法理论对比大模型与人类写新闻的差异。
Comparing LLM-generated and human-authored news text using formal syntactic theory
- 基于头驱动短语结构语法分析文本结构
- 发现大模型与人类在句法类型分布上存在系统性差异
- 适合关注语言生成机制与人机写作比较的研究者
本研究首次全面对比六种大语言模型生成的《纽约时报》风格文本与真实人类撰写的《纽约时报》文本。分析基于头驱动短语结构语法（HPSG）进行，通过解析文本的语法结构，揭示了人类写作与大模型生成文本在HPSG语法类型分布上的系统性差异。这些发现有助于深化对大模型与人类在《纽约时报》文体中句法行为的理解。
原文摘要 · Abstract (English)
This study provides the first comprehensive comparison of New York Times-style text generated by six large language models against real, human-authored NYT writing. The comparison is based on a formal syntactic theory. We use Head-driven Phrase Structure Grammar (HPSG) to analyze the grammatical structure of the texts. We then investigate and illustrate the differences in the distributions of HPSG grammar types, revealing systematic distinctions between human and LLM-generated writing. These findings contribute to a deeper understanding of the syntactic behavior of LLMs as well as humans, within the NYT genre.
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