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. |
| Outcome: | The proposed models outperform generic human-written and LLM-generated narratives on all metrics of engagement and textual diversity while preserving the intended moral. |
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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. |
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| Challenge: | Personalized stories are often preferred because they reflect a child's interests, experiences, and developmental needs. |
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A Survey on LLMs for Story Generation (2025.findings-emnlp)
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| Challenge: | Existing work on large language models lacks robustness, highlighting the limitations of such models. |
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| Challenge: | Large language models (LLMs) can be used to generate text data for training and evaluating other models. |
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Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews (2026.findings-acl)
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| Challenge: | Existing studies show that large language models carry implicit biases across race, gender, and religion . prior studies documented such biase based on text generation and classification tasks . |
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