So basically, this model is trained on a massive amount of text data to learn how words are used in context. It then uses that knowledge to generate new text based on what it has learned.

Here’s an example: Let’s say you give GPT the sentence “The quick brown fox jumps over the lazy dog.” Now, if you ask GPT to continue this story for you, it might come up with something like this: “One day, as the sun was setting in the sky, the quick brown fox decided to take a shortcut through the forest. However, he soon realized that there were many obstacles in his way. First, he had to jump over a small stream, which wasn’t too difficult for him since he was so agile. But then came the real challenge: a steep hill covered with thorny bushes.”

Now, let me explain how GPT works under the hood. It uses something called a Transformer architecture, which is basically a fancy way of saying that it can process multiple pieces of information at once (instead of one piece at a time like traditional models). This allows it to generate more accurate and coherent text.

But here’s where things get really cool: GPT doesn’t just learn from the words themselves, but also from their context in sentences and paragraphs. So if you give it the sentence “The quick brown fox jumps over the lazy dog,” it will not only remember that “quick” is an adjective describing a fox, but also that “jumps” comes after “fox” and before “over.” This helps GPT generate more natural-sounding text because it can better understand how words are used in context.

Now, the differences between GPT and its predecessor, BERT (Bidirectional Encoder Representations from Transformers). While both models use a similar architecture, they have different training objectives: GPT is trained to predict the next word in a sequence, while BERT is trained to understand context by looking at words in both directions.

This means that BERT can better handle tasks like question answering and sentiment analysis because it has more information about the surrounding text. However, GPT is still great for generating new text based on what it has learned from existing data.

The rise of decoder-only Transformers in language modeling with GPT (and its bigger brother, GPT-2). It’s like having a personal assistant who can write your emails and articles for you, but without all the ***** human errors and inconsistencies.