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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