The transformer model uses something called an “attention mechanism” to help it understand the relationships between different parts of the text.

Here’s how it works: imagine you have a sentence with multiple words, like this one: “The quick brown fox jumps over the lazy dog.” Now let’s say your computer is trying to figure out what word comes next in that sentence (because computers are really good at predicting things).

To do this, the transformer model uses a bunch of different layers and circuits. But for our purposes, we can focus on one specific part: the attention mechanism. This works by allowing the computer to “pay more attention” to certain words in the sentence than others. For example, if it’s trying to figure out what comes next after “fox,” it might pay extra attention to the word “jumps.”

So how does this work? Well, let’s say we have a vector (which is just a fancy math term for a list of numbers) that represents each word in our sentence. For example:

– “The” would be represented by [0.2, -0.5, 0.1] or something like that.
– “quick” might be represented by [-0.8, 0.3, 0.6].
– And so on for all the other words in our sentence.

Now let’s say we want to figure out what word comes next after “fox.” To do this, the transformer model uses a special function called an attention mechanism. This works by taking the vector that represents “jumps” and comparing it to each of the vectors for all the other words in our sentence (like “the,” “quick,” etc.).

The computer then calculates something called a “similarity score” between these two vectors, which tells us how closely related they are. For example:

– If we compare the vector for “jumps” to the vector for “fox,” we might get a similarity score of 0.9 (which means they’re really similar).
– But if we compare the vector for “jumps” to the vector for “lazy,” we might only get a similarity score of 0.2 (which means they’re not as closely related).

Based on these similarity scores, the transformer model can then figure out which word is most likely to come next in our sentence. And that’s how attention mechanisms work!