# Biased Tales: Cultural and Topic Bias in Generating Children's Stories

arXiv: https://arxiv.org/abs/2509.07908 · HTML: https://mlsanity.com/paper/2509.07908/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://mlsanity.com/paper/2305.02547/) (2023)
- ["Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters](https://mlsanity.com/paper/2310.09219/) (2023)
- [BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages](https://mlsanity.com/paper/2406.09948/) (2024)
- [Are Large Language Models Capable of Generating Human-Level Narratives?](https://mlsanity.com/paper/2407.13248/) (2024)
- [CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting](https://mlsanity.com/paper/2404.10199/) (2024)
- [On the steerability of large language models toward data-driven personas](https://mlsanity.com/paper/2311.04978/) (2023)
- [Uncovering Implicit Gender Bias in Narratives through Commonsense Inference](https://mlsanity.com/paper/2109.06437/) (2021)
- [Mathemyths: Leveraging Large Language Models to Teach Mathematical Language through Child-AI Co-Creative Storytelling](https://mlsanity.com/paper/2402.01927/) (2024)
- [Know Your Audience: Do LLMs Adapt to Different Age and Education Levels?](https://mlsanity.com/paper/2312.02065/) (2023)
- [Beyond Flesch-Kincaid: Prompt-based Metrics Improve Difficulty Classification of Educational Texts](https://mlsanity.com/paper/2405.09482/) (2024)
- [Exploring Parent's Needs for Children-Centered AI to Support Preschoolers' Interactive Storytelling and Reading Activities](https://mlsanity.com/paper/2401.13804/) (2024)
- [MirrorStories: Reflecting Diversity through Personalized Narrative Generation with Large Language Models](https://mlsanity.com/paper/2409.13935/) (2024)
- [KidLM: Advancing Language Models for Children -- Early Insights and Future Directions](https://mlsanity.com/paper/2410.03884/) (2024)

## Built on by (most cited first, 15 of 15)

- [RoleConflictBench: A Benchmark of Role Conflict Scenarios for Evaluating LLMs' Contextual Sensitivity](https://mlsanity.com/paper/2509.25897/) (2025)
- [Alignment Reduces Expressed but Not Encoded Gender Bias: A Unified Framework and Study](https://mlsanity.com/paper/2603.24125/) (2026)
- [Controlling Distributional Bias in Multi-Round LLM Generation via KL-Optimized Fine-Tuning](https://mlsanity.com/paper/2604.05756/) (2026)
- [Lessons Without Borders? Evaluating Cultural Alignment of LLMs Using Multilingual Story Moral Generation](https://mlsanity.com/paper/2604.08797/) (2026)
- [Cultural Authenticity: Comparing LLM Cultural Representations to Native Human Expectations](https://mlsanity.com/paper/2604.03493/) (2026)
- [BavGround: A Benchmark for Regional Cultural Grounding and Dialect Competence in Bavarian](https://mlsanity.com/paper/2608.12894/) (2026)
- [The Straight and Narrow: Do LLMs Possess an Internal Moral Path?](https://mlsanity.com/paper/2601.10307/) (2026)
- [Pragmatics Meets Culture: Culturally-adapted Artwork Description Generation and Evaluation](https://mlsanity.com/paper/2604.02557/) (2026)
- [BIASEDTALES-ML: A Multilingual Dataset for Analyzing Narrative Attribute Distributions in LLM-Generated Stories](https://mlsanity.com/paper/2604.17008/) (2026)
- [Neutrality Bites: Gender Representation in AI-Generated Animal Stories](https://mlsanity.com/paper/2606.07969/) (2026)
- [Marginal Alignment Does Not Guarantee Joint-Distribution Fidelity: An Official-Reference Audit of Nemotron-Personas-Korea with Cross-Locale Replication](https://mlsanity.com/paper/2606.12433/) (2026)
- [Constrained Semantic Decompression in LLMs through Persian Proverb-Conditioned Story Generation](https://mlsanity.com/paper/2606.12599/) (2026)
- [Characterizing Cultural Localization in AI-Generated Stories](https://mlsanity.com/paper/2606.14626/) (2026)
- [Symphony of Bias: Exploring Gender Associations with Musical Instruments in Multimodal LLMs](https://mlsanity.com/paper/2607.26355/) (2026)
- [CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systems](https://mlsanity.com/paper/2609.22494/) (2026)

## Abstract (arXiv)

Stories play a pivotal role in human communication, shaping beliefs and morals, particularly in children. As parents increasingly rely on large language models (LLMs) to craft bedtime stories, the presence of cultural and gender stereotypes in these narratives raises significant concerns. To address this issue, we present Biased Tales, a comprehensive dataset designed to analyze how biases influence protagonists' attributes and story elements in LLM-generated stories. Our analysis uncovers striking disparities. When the protagonist is described as a girl (as compared to a boy), appearance-related attributes increase by 55.26%. Stories featuring non-Western children disproportionately emphasize cultural heritage, tradition, and family themes far more than those for Western children. Our findings highlight the role of sociocultural bias in making creative AI use more equitable and diverse.