Summary of Mirrorstories: Reflecting Diversity Through Personalized Narrative Generation with Large Language Models, by Sarfaroz Yunusov et al.
MirrorStories: Reflecting Diversity through Personalized Narrative Generation with Large Language Models
by Sarfaroz Yunusov, Hamza Sidat, Ali Emami
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study explores the effectiveness of Large Language Models (LLMs) in creating personalized “mirror stories” that reflect individual readers’ identities, addressing the lack of diversity in literature. The researchers 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. They demonstrate that LLMs can effectively incorporate diverse identity elements into narratives, with human evaluators identifying personalized elements in the stories with high accuracy. The study evaluates the effectiveness of MirrorStories against generic narratives, finding that personalized LLM-generated stories outscore generic ones across all metrics of engagement (average ratings of 4.22 versus 3.37 on a 5-point scale). Additionally, the study provides analyses including bias assessments and a study on integrating images into personalized stories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses big computers to create special stories that are just for you! These “mirror stories” have words and ideas that match who you are, like your name, gender, age, and what you like. The researchers made 1,500 of these stories and showed them to people to see if they liked them better than regular stories. Surprisingly, the special computer-made stories were a hit! People loved them more (4.22 out of 5) compared to stories written by humans or made by computers without personal details. The study also looked at whether these special stories had too much of one kind of idea and found that they actually have lots of different ideas like real-life stories. |
