These babies are like the brain of a computer that can understand what you say to it, but instead of just spitting out answers, they can also generate their own responses based on what they’ve learned from all those books and articles they’ve read.

So how do these self-attention layers work? Well, let me break it down for ya like a boss:

First off, we have our input sequence (let’s call it X) which is made up of words or characters that the model needs to pay attention to in order to understand what’s going on. This could be anything from a sentence in a book to an article about cats wearing hats.

Next, we take this input sequence and feed it through a series of transformations (called “layers”) which help us extract important features or patterns that can be used for prediction or classification tasks. These layers are like little mini-brains inside the model’s brain that allow it to process information more efficiently and accurately.

Now, here comes the cool part: self-attention! This is where things get really interesting because instead of just looking at each word in isolation (like a regular neural network would do), we can actually pay attention to how different words relate to one another based on their context within the input sequence.

For example, let’s say our input sequence is: “The cat sat on the mat.” When we apply self-attention, the model will look at each word and figure out which other words it should pay more attention to in order to better understand what’s going on. In this case, since “cat” and “mat” are both related to the action of sitting (which is described by the verb “sat”), they might receive higher scores for self-attention than other words that don’t have as much contextual relevance.

This process can be repeated multiple times in order to extract even more complex patterns or relationships between different parts of the input sequence, which can then be used to make predictions about new data (like whether a given sentence is positive or negative) based on what we already know from our training set.

Self-attention layers in large language models: the brainy little mini-brains that allow us to understand and generate text with greater accuracy and efficiency than ever before.