KidLM: Advancing Language Models for Children – Early Insights and Future Directions

This paper introduces KidLM, a language model specifically designed for children through domain-specific pre-training data collection and a novel Stratified Masking training objective. The work evaluates the model's ability to understand age-appropriate text, maintain safety by avoiding stereotypes, and capture children's unique preferences.

Recent studies highlight the potential of large language models in creating educational tools for children, yet significant challenges remain in maintaining key child-specific properties such as linguistic nuances, cognitive needs, and safety standards. In this paper, we explore foundational steps toward the development of child-specific language models, emphasizing the necessity of high-quality pre-training data. We introduce a novel user-centric data collection pipeline that involves gathering

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