GPT生成的白宫演讲风格与真实总统演讲差异显著，偏乐观、用词抽象。
How good is GPT at writing political speeches for the White House?
- 对比真实总统演讲，GPT更频繁使用'我们'，句式更长但篇幅更短。
- GPT倾向使用政治、象征和抽象词汇，整体语气更乐观。
- 即使模仿特定作者风格，GPT生成内容仍明显不同于真迹，适合关注语言模型局限的研究者。
大型语言模型（LLM）能够根据用户请求生成文本。本研究分析了名为GPT的LLM所生成演讲的写作风格，并与里根至拜登任期内的国情咨文进行对比。研究选取了从里根到拜登的多届总统演讲，分别与GPT-3.5和GPT-4.0生成的内容进行比较。结果显示，相较于真实总统演讲，GPT更频繁使用代词'we'，平均句长更长但整体篇幅更短。此外，GPT更倾向于使用政治性（如'president'）、象征性（如'freedom'）及抽象术语（如'freedom'），整体语调更为乐观。即便对GPT施加特定作者风格的约束，其生成内容仍与目标作者的真实演讲存在明显差异。两种GPT版本在特征上各不相同，但均与真实的总统演讲有显著区别。
原文摘要 · Abstract (English)
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.
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