Biased Tales: Cultural and Topic Bias in Generating Children’s Stories (2025.emnlp-main)
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| Challenge: | Personalized stories are often preferred because they reflect a child's interests, experiences, and developmental needs. |
| Approach: | They analyze a dataset to examine how biases influence protagonists’ attributes and story elements in LLM-generated stories. |
| Outcome: | The proposed dataset shows that gender stereotypes influence protagonist attributes and story elements in LLM-generated stories. |
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| Challenge: | Existing studies on the use of Large Language Models (LLMs) focus primarily on English, leaving the cross-lingual generalization of aligned behavior underexplored. |
| Approach: | They propose a structured generator-extractor pipeline and a multi-dimensional distributional analysis framework to examine how narrative attributes vary across languages, models, and social conditions. |
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Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children’s Fairy Tales (2023.acl-long)
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| Challenge: | Social biases and stereotypes are embedded in our culture through their presence in our stories. |
| Approach: | They propose a computational pipeline that automatically extracts a story’s temporal narrative verb-based event chain for each of its characters as well as character attributes such as gender. |
| Outcome: | The proposed framework extracts a story’s verb-based event chain for each of its characters as well as character attributes such as gender. |
Cross-Cultural Analysis of Human Values, Morals, and Biases in Folk Tales (2023.emnlp-main)
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| Challenge: | Existing studies on folk tales focus on European tales, ignoring large swaths of the world's diverse cultures. |
| Approach: | They compile a corpus of over 1,900 folk tales originating from 27 diverse cultures across six continents and employ lexicon-based correlation analyses to examine human values, morals, and gender biases. |
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MirrorStories: Reflecting Diversity through Personalized Narrative Generation with Large Language Models (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are used to create personalized “mirror stories” that reflect and resonate with individual readers’ identities. |
| Approach: | They propose to use Large Language Models to create personalized “mirror stories” that reflect and resonate with individual readers’ identities. |
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Tales of Morality: Comparing Human- and LLM-Generated Moral Stories from Visual Cues (2025.findings-emnlp)
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| Challenge: | a recent study has found that stories are central to how humans communicate moral values . |
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CASPER in the Machine: Insights into Character Variety in LLM-Generated Stories (2026.acl-long)
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| Challenge: | Increasing numbers of authors are using AI to assist in the process of writing stories. |
| Approach: | They analyze 8 category-pairs of character that assess how characters are portrayed in short stories . they find similarities between LLMs and human-written stories based on categories . |
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Uncovering Implicit Gender Bias in Narratives through Commonsense Inference (2021.findings-emnlp)
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| Challenge: | Pre-trained language models learn harmful biases from their training corpora and may repeat these biase if used for generation. |
| Approach: | They focus on gender biases associated with the protagonist in model-generated stories and use a commonsense reasoning engine to uncover them. |
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Global Voices, Local Biases: Socio-Cultural Prejudices across Languages (2023.emnlp-main)
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| Challenge: | Existing studies on human biases are heavily skewed towards Western and European languages . despite growing interest in language models, there are several shortcomings in the literature . |
| Approach: | They scale the Word Embedding Association Test to 24 languages and add culturally relevant information for each language. |
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Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception (2025.coling-main)
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| Challenge: | Detecting media bias is critical due to the spread of misinformation and disinformation on social media platforms. |
| Approach: | They investigate the presence and nature of bias within large language models and its consequential impact on media bias detection. |
| Outcome: | The proposed debiasing strategies include prompt engineering and model fine-tuning. |
Richer Output for Richer Countries: Uncovering Geographical Disparities in Generated Stories and Travel Recommendations (2025.findings-naacl)
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| Challenge: | a large body of work examines language models for biases concerning gender, race, occupation and religion . however, the impact of the encoded geographical knowledge on real-world applications has not been documented . |
| Approach: | They examine large language models for two common scenarios that require geographical knowledge: travel recommendations and geo-anchored story generation. |
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