Natural Language Processing (NLP) systems have made their way into the daily lives of society. These systems range from digital assistants to language translation to large language models (such as ChatGPT) which have grabbed headlines in recent years. When looking at the recent advances in large language models (LLM), there has been a strong trend toward more data and bigger models are better. In this talk, we will examine different approaches to building NLP systems which instead draw inspiration from human language acquisition to improve NLP systems while using less computational resources and data.