Understanding Large Language Models Without Math and Jargon

The models use cutting-edge machine learning algorithms that are more capable than traditional approaches.

The models are able to learn from a small amount of training data due to their advanced architecture.

The training process is highly advanced and complex.

The models are trained on a vast amount of data, containing billions of words. (correct)

They have steadily increased the size of their language models, along with the amount of training data and computing power used. (correct)

They have focused more on improving the underlying algorithms rather than increasing model size.

They have shifted their focus to developing specialized models for narrow tasks rather than general-purpose language models.

They have decreased the size of their models to improve efficiency.

Increasing any one of these factors will lead to a linear improvement in model performance.

There is no clear relationship between these factors, as they are largely independent.

Increasing model size, dataset size, and training compute power all lead to exponential improvements in model performance.

Increasing these factors leads to a power-law improvement in model performance, as described in the text. (correct)

<p>GPT-1 had 768-dimensional word vectors and 12 layers, for a total of 117 million parameters.</p> Signup and view all the answers

<p>The massive scale of the training data used, containing billions of words.</p> Signup and view all the answers