What makes a language model large?

A language model is considered 'large' when it has been trained on a 'massive amount of text data' and has a 'large number of parameters', which are the model's learning variables.

Think of it like a library: a small library has a few books, while a large library has millions of books. Similarly, a small language model is trained on a small amount of text data, while a large language model is trained on a vast amount of text data.

To be specific, a large language model typically:

- Has been trained on billions of words or more.

- Has hundreds of millions or billions of parameters.

- Requires significant computational power and memory to run.

This large scale allows the model to learn more about language, including nuances and complexities, and generate more coherent and natural-sounding text. Large language models are powerful tools for many applications, like language translation, text summarization, and content generation.