5 Questions
What is the primary reason that large language models like GPT-3 can perform such a wide variety of complex tasks?
The models are trained on a vast amount of data, containing billions of words.
How has OpenAI's approach to training large language models changed over the last five years?
They have steadily increased the size of their language models, along with the amount of training data and computing power used.
What is the relationship between model size, dataset size, and training compute power for large language models like GPT-3?
Increasing these factors leads to a power-law improvement in model performance, as described in the text.
What was the size and architecture of OpenAI's first large language model, GPT-1?
GPT-1 had 768-dimensional word vectors and 12 layers, for a total of 117 million parameters.
What is the key factor that has enabled large language models like GPT-3 to achieve such impressive performance?
The massive scale of the training data used, containing billions of words.
Learn about large language models in a simple and non-technical way without the need for complex math and jargon. This primer provides an easy-to-understand explanation of how these models work.
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