Background:

High Level Summary:

Attention allows the model to focus on the most relevant parts of the input when generating the output. It works by calculating scores for each word in the input to determine their relevance. The words with the highest scores are given more attention.

Illustrations:

Imagine you are reading a book and come across an important sentence. You pay more attention to the key words in that sentence. Attention works similarly in transformers.

The model calculates an attention score for each input word. The words with the highest scores get the most focus.

Simplified Summary:

Here is a simplified summary of the key concepts required to build an attention-based language model like chatGPT or Claude:

Input Embedding Layer: Convert input words/tokens into numerical vector representations. This allows the model to understand semantic relationships between words. Self-Attention Layers: Relate different words in a sentence to each other, helping the model understand context. The core mechanism is computing compatibility scores between words and using those to aggregate information. Feedforward Layers: Process the intermediate representations from the attention layers to extract higher-level features. Output Layer: Convert the features from the last feedforward layer into predicted next-word probabilities. To explain this visually:

Input Words         ->   Input Embedding Layer   -> Embedded Word Vectors

Embedded Vectors    ->   Self-Attention Layer    -> Contextual Vectors  

Context Vectors     ->   Feedforward Layer      -> Higher-Level Features

Higher-Level Features -> Output Layer       -> Next Word Probabilities

The key is stacking multiple self-attention and feedforward layers, so the model can learn complex relationships between words and sentence structure.

This shows the basic steps. To build a full model, we'd stack multiple self-attention and feedforward layers between the input and output. Let me know if you would like me to provide an example doing that!