"Recent advancements enabling the creation of smaller, yet highly capable, Large Language Models (LLMs) often rely on a technique known as "Distillation."

This process involves transferring learned knowledge and capabilities from a large, complex 'teacher' model to a smaller 'student' model.

The key advantage is that the student model can then perform tasks effectively while requiring significantly fewer computational resources.

This technological process finds a compelling analogy in the way human knowledge has been transmitted across generations.

Throughout history, human societies have encoded their collective experiences, discoveries, and understanding of the world into the structures and vocabulary of language.

By learning the language of their community, subsequent generations gain efficient access to this vast repository of accumulated knowledge, bypassing the need to rediscover everything independently.

In essence, language acts as a medium for 'distilling' the wisdom and insights of predecessors into a compact and accessible format for newcomers, much like the teacher LLM distills its competence for the student LLM."