GPT-4o-mini生成的全球故事高度同质，偏好稳定与怀旧。
AI-generated stories favour stability over change: homogeneity and cultural stereotyping in narratives generated by gpt-4o-mini
- 用236国地名生成1.18万篇故事，统一采用小镇返乡+传统复兴结构
- 90%故事淡化真实冲突，几乎无浪漫情节，叙事张力被消解
- 揭示AI叙事隐性偏见：以文化稳定为名的全球同质化输出
为检验以盎格鲁-美国文本为主训练的语言模型能否生成具有文化相关性的故事，我们向OpenAI的gpt-4o-mini发送提示词“写一篇约1500字的{民族称谓}潜在故事”，生成了11,800篇故事（每国50篇，共236个国家）。尽管包含表面国家符号和主题，这些故事普遍遵循单一叙事结构：主人公生活在或返回小城镇，通过重连传统与组织社区活动解决微小冲突。现实冲突被净化，浪漫几乎消失，叙事张力被削弱，转而强调怀旧与和解。结果导致叙事同质化，形成一种优先稳定、轻视变革的合成想象。我们认为，AI生成叙事的结构性同质化构成一种新型偏见——叙事标准化，应与传统的表征偏见并列审视。该发现对文学研究、叙事学、批判性AI研究、NLP及提升生成式AI的文化适配性均有重要意义。
原文摘要 · Abstract (English)
Can a language model trained largely on Anglo-American texts generate stories that are culturally relevant to other nationalities? To find out, we generated 11,800 stories - 50 for each of 236 countries - by sending the prompt "Write a 1500 word potential {demonym} story" to OpenAI's model gpt-4o-mini. Although the stories do include surface-level national symbols and themes, they overwhelmingly conform to a single narrative plot structure across countries: a protagonist lives in or returns home to a small town and resolves a minor conflict by reconnecting with tradition and organising community events. Real-world conflicts are sanitised, romance is almost absent, and narrative tension is downplayed in favour of nostalgia and reconciliation. The result is a narrative homogenisation: an AI-generated synthetic imaginary that prioritises stability above change and tradition above growth. We argue that the structural homogeneity of AI-generated narratives constitutes a distinct form of AI bias, a narrative standardisation that should be acknowledged alongside the more familiar representational bias. These findings are relevant to literary studies, narratology, critical AI studies, NLP research, and efforts to improve the cultural alignment of generative AI.
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