Update README.md

README.md

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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.",
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+## Contributors
+- Mir Tafseer Nayeem (mnayeem@ualberta.ca)
+- Davood Rafiei (drafiei@ualberta.ca)