Large Language Models (LLMs) are capable of generating text that is similar
to or surpasses human quality. However, it is unclear whether LLMs tend to
exhibit distinctive linguistic styles akin to how human authors do. Through a
comprehensive linguistic analysis, we compare the vocabulary, Part-Of-Speech
(POS) distribution, dependency distribution, and sentiment of texts generated
by three of the most popular LLMS today (GPT-3.5, GPT-4, and Bard) to diverse
inputs. The results point to significant linguistic variations which, in turn,
enable us to attribute a given text to its LLM origin with a favorable 88\%
accuracy using a simple off-the-shelf classification model. Theoretical and
practical implications of this intriguing finding are discussed.

通过语言分析，对比了目前三种最流行的大型语言模型（GPT-3.5、GPT-4 和 Bard）生成的文本与不同输入的词汇、词性分布、依存分布和情感，结果显示存在显著的语言变化，通过简单的模型分类可以以 88% 的准确率将文本归属于相应的大型语言模型。讨论了这一有趣发现的理论和实际意义。