Understanding Decoder-Only Transformers Part 1: Masked Self-Attention

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Decoder-Only Transformers

In this article, we will explore decoder-only transformers.

Decoder-only transformers are a specific type of transformer architecture used in systems like ChatGPT.

Masked Self-Attention

Decoder-only transformers use a mechanism called masked self-attention.

Masked self-attention works by measuring how similar each word is to itself and to the words that come before it in the sentence.

For example:

“The pizza came out of the oven and it tasted good.”

When processing the word “pizza”, masked self-attention only considers the preceding word “The”.

Key Difference

Unlike standard self-attention, masked self-attention does not allow a word to look at future words. It can only attend to the current word and the words that come before it.

Because of this, it is also called an auto-regressive method.

An auto-regressive method is a way of predicting values step by step, where each prediction depends on the previous outputs.

The model uses its past predictions as input to generate the next output
It builds the final result one step at a time
Each step depends on what was generated before it, not what comes after

We will explore this concept in more detail in the next article.

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