Episode 27: How AI Understands Sentences — The Secret Lies in the Order of Words

How Does AI Understand the Meaning of Sentences?

“Does AI really understand what it’s reading?”
Many people may have wondered about this. It’s true that modern AI can generate natural-sounding sentences and respond accurately to questions. But in reality, AI doesn’t start out understanding word order or context.

In particular, with a system called “Transformer,” AI can’t naturally sense the order in which words appear. This is where “positional encoding” comes into play.

It might sound like a technical term, but thanks to this mechanism, AI can tell the difference between “I ate breakfast” and “Breakfast ate me.” In other words, it learns that changing the order of words changes the meaning.

Why Transformer Alone Isn’t Enough – The Role of Positional Encoding

The Transformer model has the ability to view an entire sentence all at once. It’s like being able to glance over an entire page of a book in one go. However, just seeing everything at once doesn’t mean it knows what came first or last.

Imagine laying out word cards randomly across a table. You can see all the words, but you wouldn’t know in what order they were originally arranged.

That’s why we need to tell the AI something like “this word was third” or “that one came first.” That’s exactly what positional encoding does. With this information, AI can learn where each word appeared in a sentence.

Hints from Word Order That Help Create Natural Sentences

There are various ways to assign this positional information. One commonly used method involves mathematical waves called sine and cosine waves. While that may sound complex, you can think of it as giving each word a rhythm or shade that reflects its place in the sentence.

This allows AI to pick up clues like “this word was at the beginning” or “that one was near the end.” From there, it tries to understand how the whole sentence flows and what it means.

Humans do something similar when reading long texts like novels—we naturally follow the timeline and sequence of events. But imagine if all the pages were shuffled—no matter how interesting the story is, it would be hard to follow if things were out of order.

You can think of positional encoding as writing page numbers on those scattered pages so they can be read in the correct sequence.

Of course, this method has its limits too. For very long texts, these position clues alone may not be enough to fully capture subtle context. That’s why newer types of positional encoding and improved models are being developed (we’ll explore those in another article).

Still, it’s clear that this simple yet powerful idea plays a key role in making today’s natural-sounding text generation by AI possible.

Language Has Flow – Creating That Sense for AI

When working with language, having a sense of “order” is more important than we might think. And behind-the-scenes support for building that sense comes from positional encoding.

It may seem like an unremarkable part of the system, but without it, nothing would work properly—like stage crew managing lighting and sound so performers can shine on stage.

In our next article, we’ll take a closer look at how AI actually writes sentences—the mysterious process behind choosing words and forming phrases. We hope you’ll join us again for another peek behind the curtain.