Transformer Architecture

Most modern LLM applications, including ChatGPT, Gemini, and Claude, are built on the transformer architecture.

Before transformers, RNNs and LSTMs were the standard models for language tasks. Transformers replaced them by overcoming many of the limitations of these earlier architectures, making them more effective for modern natural language processing tasks.

Why Transformers Replaced RNNs

They process text one word at a time, and because of that, they suffer from the vanishing gradient problem, which causes them to lose memory of things said earlier in a sentence. Read a long paragraph with an RNN, and it might genuinely forget what the first sentence said by the time it reaches the last one. 

Transformers fix this by using attention to access every word directly, instead of trying to compress everything and carry it forward step by step. On top of that, transformers process an entire sentence all at once instead of word by word, which is a big reason training them is so much faster, especially with strong hardware behind it. 

Basically, an RNN reads a book one word at a time and tries to remember everything. A transformer reads the whole page in one glance and instantly sees how every word connects to every other word.

Attention and Self-Attention Mechanisms

1. Attention Mechanism

The attention mechanism allows a transformer to focus on the most relevant parts of the input when producing an output. It assigns different importance to different words, helping the model understand context. For example, in the sentence "The trophy did not fit in the suitcase because it was too big," attention helps determine what the word "it" refers to by considering the surrounding words.

2. Self-Attention Mechanism

Self-attention allows every word in a sentence to interact with every other word, enabling the model to understand relationships across the entire sequence. Transformers also use multiple attention heads, with each head learning different relationships such as grammar, meaning, or word position. This helps the model capture both nearby and long-range dependencies effectively.

Encoder and Decoder Structure