KidLM: Advancing Language Models for Children -- Early Insights and Future Directions
<|reference_start|>KidLM: Advancing Language Models for Children -- Early Insights and Future Directions: 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 and validating a corpus specifically written for and sometimes by children. Additionally, we propose a new training objective, Stratified Masking, which dynamically adjusts masking probabilities based on our domain-specific child language data, enabling models to prioritize vocabulary and concepts more suitable for children. Experimental evaluations demonstrate that our model excels in understanding lower grade-level text, maintains safety by avoiding stereotypes, and captures children's unique preferences. Furthermore, we provide actionable insights for future research and development in child-specific language modeling.<|reference_end|>
@article{nayeem2024kidlm:,
    title={KidLM: Advancing Language Models for Children -- Early Insights and
  Future Directions},
    author={Mir Tafseer Nayeem, Davood Rafiei},
    journal={arXiv preprint arXiv:2410.03884},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03884},
    primaryClass={cs.CL cs.AI cs.CY cs.HC}
}