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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T KidLM: Advancing Language Models for Children – Early Insights and Future Directions
%A Nayeem, Mir Tafseer
%A Rafiei, Davood
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F nayeem-rafiei-2024-kidlm
%X 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.
%U https://aclanthology.org/2024.emnlp-main.277
%P 4813-4836

Markdown (Informal)

[KidLM: Advancing Language Models for Children – Early Insights and Future Directions](https://aclanthology.org/2024.emnlp-main.277) (Nayeem & Rafiei, EMNLP 2024)

• KidLM: Advancing Language Models for Children – Early Insights and Future Directions (Nayeem & Rafiei, EMNLP 2024)

ACL

• Mir Tafseer Nayeem and Davood Rafiei. 2024. KidLM: Advancing Language Models for Children – Early Insights and Future Directions. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4813–4836, Miami, Florida, USA. Association for Computational Linguistics.