Original Post

When ChatGPT was introduced last fall, it sent shockwaves through the technology industry and the larger world. Machine learning researchers had been experimenting with large language models (LLMs) for a few years by that point, but the general public had not been paying close attention and didn’t realize how powerful they had become.

Today almost everyone has heard about LLMs, and tens of millions of people have tried them out. But, still, not very many people understand how they work.

If you know anything about this subject, you’ve probably heard that LLMs are trained to “predict the next word,” and that they require huge amounts of text to do this. But that tends to be where the explanation stops. The details of how they predict the next word is often treated as a deep mystery.

One reason for this is the unusual way these systems were developed. Conventional software is created by human programmers who give computers explicit, step-by-step instructions. In contrast, ChatGPT is built on a neural network that was trained using billions of words of ordinary language.

As a result, no one on Earth fully understands the inner workings of LLMs. Researchers are working to gain a better understanding, but this is a slow process that will take years—perhaps decades—to complete.

Still, there’s a lot that experts do understand about how these systems work. The goal of this article is to make a lot of this knowledge accessible to a broad audience. We’ll aim to explain what’s known about the inner workings of these models without resorting to technical jargon or advanced math.

We’ll start by explaining word vectors, the surprising way language models represent and reason about language. Then we’ll dive deep into the transformer, the basic building block for systems like ChatGPT. Finally, we’ll explain how these models are trained and explore why good performance requires such phenomenally large quantities of data.

Word vectors

To understand how language models work, you first need to understand how they represent words. Human beings represent English words with a sequence of letters, like C-A-T for cat. Language models use a long list of numbers called a word vector. For example, here’s one way to represent cat as a vector:

[0.0074, 0.0030, -0.0105, 0.0742, 0.0765, -0.0011, 0.0265, 0.0106, 0.0191, 0.0038, -0.0468, -0.0212, 0.0091, 0.0030, -0.0563, -0.0396, -0.0998, -0.0796, …, 0.0002]

(The full vector is 300 numbers long—to see it all click here and then click “show the raw vector.”)

Why use such a baroque notation? Here’s an analogy. Washington DC is located at 38.9 degrees North and 77 degrees West. We can represent this using a vector notation:

• Washington DC is at [38.9, 77]
• New York is at [40.7, 74]
• London is at [51.5, 0.1]
• Paris is at [48.9, -2.4]

This is useful for reasoning about spatial relationships. You can tell New York is close to Washington DC because 38.9 is close to 40.7 and 77 is close to 74. By the same token, Paris is close to London. But Paris is far from Washington DC.

Language models take a similar approach: each word vector¹ represents a point in an imaginary “word space,” and words with more similar meanings are placed closer together. For example, the words closest to cat in vector space include dog, kitten, and pet. A key advantage of representing words with vectors of real numbers (as opposed to a string of letters, like “C-A-T”) is that numbers enable operations that letters don’t.

Words are too complex to represent in only two dimensions, so language models use vector spaces with hundreds or even thousands of dimensions. The human mind can’t envision a space with that many dimensions, but computers are perfectly capable of reasoning about them and producing useful results.

…

Transforming word vectors into word predictions

GPT-3, the model behind the original version of ChatGPT², is organized into dozens of layers. Each layer takes a sequence of vectors as inputs—one vector for each word in the input text—and adds information to help clarify the meaning of that word and better predict which word might come next.

Let’s start by looking at a stylized example:

Each layer of an LLM is a transformer, a neural network architecture that was first introduced by Google in a landmark 2017 paper.

The model’s input, shown at the bottom of the diagram, is the partial sentence “John wants his bank to cash the.” These words, represented as word2vec-style vectors, are fed into the first transformer.

The transformer figures out that wants and cash are both verbs (both words can also be nouns). We’ve represented this added context as red text in parentheses, but in reality the model would store it by modifying the word vectors in ways that are difficult for humans to interpret. These new vectors, known as a hidden state, are passed to the next transformer in the stack.

The second transformer adds two other bits of context: it clarifies that bank refers to a financial institution rather than a river bank, and that his is a pronoun that refers to John. The second transformer produces another set of hidden state vectors that reflect everything the model has learned up to that point.

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