Token Embeddings vs Positional Embeddings

Imagine you're trying to understand a new language by studying a dictionary and a map. The dictionary would tell you the meaning of each word, like "dog" means a furry friend, "cat" means a playful hunter, and so on. But just knowing the individual words isn't enough, right? You also need to know where they are in a sentence to understand what they mean together.

That's where the map comes in. It shows you the order of words in a sentence, just like the map shows you the order of streets in a city. This order gives you context: "dog chases cat" means something different than "cat chases dog."

In computers, this "dictionary" and "map" are called token embeddings and positional encodings. Token embeddings are like mini-descriptions for each word, capturing its meaning and relationships to other words. Positional encodings are like the map, showing the computer where each word is in the sentence.

Now, here's the cool part: both the dictionary and the map work together to help you understand the language fully. Just knowing the word "meow" doesn't tell you if it's a happy cat or a scared one. But if you see it's at the end of a sentence with an exclamation mark, you know it's probably a happy meow!

The same goes for computers. By combining token embeddings and positional encodings, they can understand the meaning of words not just individually, but also in relation to each other and their positions in a sentence. This helps them do amazing things like translate languages, write creative stories, and even answer your questions in a way that makes sense!

Remember, the specific way computers do this can be complicated, but the basic idea is like using a dictionary and a map together. It's all about understanding the meaning of words and how they work together to make language work!