Language Model Distillation

Language model distillation aims to transfer knowledge from large, computationally expensive language models ("teachers") to smaller, more efficient models ("students"), enabling deployment on resource-constrained devices. Current research focuses on improving distillation efficiency through techniques like zero-shot prompting, maximizing mutual information between teacher and student representations, and developing methods that bypass the need for intermediate layers or labeled data. These advancements are significant because they reduce the cost and complexity of deploying powerful language models across various applications, from fact verification to spoken language understanding and even robotics.