用大模型生成个性化故事，让读者在文字中看见自己。
MirrorStories: Reflecting Diversity through Personalized Narrative Generation with Large Language Models
- 基于用户身份信息生成定制化短篇故事，融合姓名、性别、年龄等要素。
- 真人评估显示，个性化故事在吸引力上评分达4.22（满分5），显著优于通用故事。
- 适合关注包容性内容创作、教育或心理陪伴场景的开发者与研究者。
本研究探讨大型语言模型（LLMs）在生成能反映并共鸣个体身份的个性化“镜像故事”方面的有效性，旨在解决文学作品中多样性缺失的问题。我们构建了包含1,500篇个性化短篇故事的镜像故事数据集（MirrorStories），融合姓名、性别、年龄、族裔、读者兴趣及故事寓意等元素。通过26位多样化的真人评委进行综合评估，发现由大模型生成的个性化故事在所有互动指标上均显著优于通用人类撰写和大模型生成的故事（平均评分4.22对比3.37，满分为5分），同时保持更高文本多样性并忠实传达预定道德主旨。研究还包含偏见分析，并探讨了将图像融入个性化故事的潜力。
原文摘要 · Abstract (English)
This study explores the effectiveness of Large Language Models (LLMs) in creating personalized "mirror stories" that reflect and resonate with individual readers' identities, addressing the significant lack of diversity in literature. We present MirrorStories, a corpus of 1,500 personalized short stories generated by integrating elements such as name, gender, age, ethnicity, reader interest, and story moral. We demonstrate that LLMs can effectively incorporate diverse identity elements into narratives, with human evaluators identifying personalized elements in the stories with high accuracy. Through a comprehensive evaluation involving 26 diverse human judges, we compare the effectiveness of MirrorStories against generic narratives. We find that personalized LLM-generated stories not only outscore generic human-written and LLM-generated ones across all metrics of engagement (with average ratings of 4.22 versus 3.37 on a 5-point scale), but also achieve higher textual diversity while preserving the intended moral. We also provide analyses that include bias assessments and a study on the potential for integrating images into personalized stories.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。