BabyLM Challenge

The BabyLM Challenge focuses on developing small, data-efficient language models inspired by human language acquisition. Research currently explores techniques like knowledge distillation, continual pre-training, and architectural modifications (e.g., selective layer processing) to improve performance with limited training data, often using developmentally-plausible corpora. This work contributes to a deeper understanding of efficient language model training and has implications for low-resource NLP applications and cognitive science, offering insights into how humans learn language.