Using large language models (LLMs), computers are able to generate a written text in response to a us er request. As this pervasive technology can be applied in numerous contexts, this study analyses the written style of one LLM called GPT by comparing its generated speeches with those of the recent US presidents. To achieve this objective, the State of the Union (SOTU) addresses written by Reagan to Biden are contrasted to those produced by both GPT-3.5 and GPT-4.o versions. Compared to US presidents, GPT tends to overuse the lemma "we" and produce shorter messages with, on average, longer sentences. Moreover, GPT opts for an optimistic tone, opting more often for political (e.g., president, Congress), symbolic (e.g., freedom), and abstract terms (e.g., freedom). Even when imposing an author's style to GPT, the resulting speech remains distinct from addresses written by the target author. Finally, the two GPT versions present distinct characteristics, but both appear overall dissimilar to true presidential messages.

翻译：利用大型语言模型（LLM），计算机能够根据用户请求生成书面文本。鉴于这项普及性技术可应用于众多场景，本研究通过比较GPT生成的演讲与近期美国总统的演讲，分析了这一LLM的写作风格。为实现这一目标，我们将里根至拜登撰写的国情咨文（SOTU）演讲与GPT-3.5和GPT-4.o版本生成的演讲进行对比。与美国总统相比，GPT倾向于过度使用词元"we"，并生成篇幅较短但平均句长较长的文本。此外，GPT选择采用乐观基调，更频繁地使用政治性（如总统、国会）、象征性（如自由）和抽象性（如自由）词汇。即使将特定作者的风格强加于GPT，生成的演讲仍与目标作者撰写的演讲存在差异。最后，两个GPT版本呈现出不同特征，但总体上均与真实的总统演讲存在明显区别。