Reconstructing Context with Sequence Models

We’ve learned about techniques like TF-IDF and GloVe that help computers understand how words are related. Think of them as colorful LEGO bricks, each representing a word. They’re great for showing which words frequently appear together. But here’s the tricky part: even advanced tools like GloVe assign the same LEGO brick to a word no matter where it’s used. So, the word “bank” looks the same whether we’re talking about a place for money or the side of a river. As we previously discussed, this is called a “static embedding.” It’s like using the same LEGO piece for a money-building and river-building set—the computer can’t tell the difference without extra clues.

To truly understand a sentence, a computer must know which words are present, the order in which they appear, and how earlier words affect the meaning of later ones. We also saw that these static embeddings are learned using neural networks that have evolved over decades. Language isn’t just a collection of words—it’s a story where the sequence and context matter. To truly grasp the meaning behind a sentence, a model must recognize how the order of words influences overall context.So, how do we make computers understand the whole story? Enter sequence models! These are like magical LEGO sets that provide you with individual bricks and remember the order in which you assemble them.

In this lesson, we’ll explore traditional sequence models in depth—from convolutional neural networks (CNNs) that capture local patterns to recurrent neural networks (RNNs) that introduce memory and, finally, to long short-term memory networks (LSTMs) that overcome the limitations of basic RNNs. We will dive into the concepts with engaging analogies and plain-English explanations of the underlying math, always tying back to how these models power modern generative AI systems.

Why sequence models?

Imagine you’re building a sentence out of LEGO bricks. In previous lessons, we learned how neural networks transform words into static embeddings using techniques like TF-IDF and GloVe. These methods give you colorful LEGO bricks with fixed shapes and colors that capture which words frequently appear together. However, these static embeddings treat every brick the same—so the word “bank” ends up with the same brick whether you’re constructing a financial district or a riverside scene.

Static embeddings are useful for capturing basic word relationships, but they don’t account for the sequential nature of language. The meaning of a ...