So, imagine you have a bunch of words and you want to feed them into your computer model so it can predict what comes next. But there’s a problem: the computer doesn’t know which word is first or last or middle-ish. That’s where positional encodings come in!

Positional encodings are like little flags that get added to each word, telling the model where it falls in the sentence. For example, if you have the words “the”, “cat”, and “sat” (in that order), the first word would get a positional encoding of [1, 0], since it’s the first word in the second position (i.e., the second slot in the input sequence). The second word would get [2, 1], because it’s the second word overall and the first word in its own little “sub-sequence” within the larger sentence. And so on.

Now that we have these positional encodings, we can feed them into our attention mechanism (which is like a fancy filter that helps the model focus on certain parts of the input). The attention mechanism looks at all the words in the sequence and decides which ones are most important for predicting what comes next. But instead of just looking at the raw text, it also takes into account the positional encodings to help it understand where each word falls within the sentence.

So, let’s say you have a sentence like “the cat sat on the mat”. The attention mechanism might look something like this:

1. First, we add our positional encodings to each word (e.g., [1, 0], [2, 1], etc.).

2. Next, we feed these encoded words into our attention mechanism along with some other information (like the previous output of the model).

3. The attention mechanism looks at all the input words and decides which ones are most important for predicting what comes next based on their positional encodings as well as any other relevant factors.

4. Finally, we use this “attention score” to weight each word’s contribution to our output prediction (i.e., if a certain word has a high attention score, it will have more influence over what the model predicts).

And that’s basically how positional encodings work in language models! It might sound complicated at first, but once you break it down into simpler terms like this, it becomes much easier to understand.