We conduct a quantitative analysis contrasting human-written English news
text with comparable large language model (LLM) output from 4 LLMs from the
LLaMa family. Our analysis spans several measurable linguistic dimensions,
including morphological, syntactic, psychometric and sociolinguistic aspects.
The results reveal various measurable differences between human and
AI-generated texts. Among others, human texts exhibit more scattered sentence
length distributions, a distinct use of dependency and constituent types,
shorter constituents, and more aggressive emotions (fear, disgust) than
LLM-generated texts. LLM outputs use more numbers, symbols and auxiliaries
(suggesting objective language) than human texts, as well as more pronouns. The
sexist bias prevalent in human text is also expressed by LLMs.

定量分析比较人类写作的英文新闻与基于 4 个 LLMa 家族的大型语言模型 (LLM) 输出的差异，结果显示了人类文本与 AI 生成文本在多个可测量的语言维度上的差异，包括形态、句法、心理测量和社会语言方面，并揭示了 LLM 生成文本中存在更多的数字、符号和助动词，以及更多的代词，人类文本中存在的性别偏见也被 LLMs 表达出来。