MirrorStories: Reflecting Diversity through Personalized Narrative Generation with Large Language Models

Sarfaroz Yunusov, Hamza Sidat, Ali Emami


Abstract
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.
Anthology ID:
2024.emnlp-main.382
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6702–6717
Language:
URL:
https://aclanthology.org/2024.emnlp-main.382
DOI:
10.18653/v1/2024.emnlp-main.382
Bibkey:
Cite (ACL):
Sarfaroz Yunusov, Hamza Sidat, and Ali Emami. 2024. MirrorStories: Reflecting Diversity through Personalized Narrative Generation with Large Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 6702–6717, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
MirrorStories: Reflecting Diversity through Personalized Narrative Generation with Large Language Models (Yunusov et al., EMNLP 2024)
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PDF:
https://aclanthology.org/2024.emnlp-main.382.pdf