2025
ACL
ACL 2025
Enriching children’s stories with LLMs: Delivering multilingual data enrichment for children’s books at scale and across markets
Abstract
AbstractThis paper presents a user-centered, empirically guided approach to multilingual metadata enrichment for children’s books. We combine LLMs with human-in-the-loop quality control in a scalable CI/CD pipeline to curate brand collections that enhance book discovery and engagement for young readers across multiple European markets. Our results demonstrate that this hybrid approach delivers high-quality, child-appropriate labels, improves user experience, and accelerates deployment in real-world production environments. This work offers practical insights for applying generative NLP in the media and publishing industry.
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Interdisciplinary Bridge
- Artificial Intelligence and Natural Language Processing
Authors
Topics
Artificial Intelligence > Core AI > Foundation Models
Machine Learning > Learning Types > Self-Supervised Learning
Natural Language Processing > Resources & Methods > Large Language Models
Artificial Intelligence > Core AI > Large Language Models
Natural Language Processing > Applications > Text Processing