MirrorStories: Reflecting Diversity through Personalized Narrative Generation with Large Language Models
It addresses the problem of limited representation in literature for diverse readers, though it is incremental as it applies existing LLM methods to a new application.
This study tackled the lack of diversity in literature by using Large Language Models to generate personalized 'mirror stories' that incorporate individual identity elements, resulting in higher engagement scores (4.22 vs. 3.37 on a 5-point scale) and greater textual diversity compared to generic narratives.
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.