How Transformers Work

Transformer models, the architecture behind GPT, are designed to predict the next element in a sequence, most commonly the next word in a sentence. They achieve this by processing an entire input sequence simultaneously, rather than one word at a time, to build a rich contextual understanding.

How Transformers Process Language

First, words are converted into numerical representations called embeddings. These embeddings are high-dimensional vectors, essentially coordinates in a vast 'embedding space' where words with similar meanings are located closer together. This allows the model to perform mathematical operations on words.

The Role of Attention

The core innovation of Transformers is the attention mechanism. This allows each word in the input sentence to weigh the importance of every other word in the same sentence. For example, when processing the word "sat," the attention mechanism helps it identify that "cat" is highly relevant. This is done by generating a 'Query' vector for the current word and 'Key' and 'Value' vectors for all other words. The Query is compared against all Keys to determine relevance, and then the corresponding Values are combined to form a context-rich representation for the original word.

Multi-Headed Attention and Layers

Transformers employ multi-headed attention, meaning multiple independent attention mechanisms (or "heads") analyze different aspects of the relationships between words simultaneously. One head might focus on grammatical dependencies, another on semantic meaning, and yet another on tone. The outputs from these heads are then combined.

These attention mechanisms are organized into multiple stacked layers. Each layer refines the contextual understanding, building increasingly abstract and nuanced representations of the input text. This deep stacking allows the model to grasp complex relationships and long-range dependencies within a sentence.

Predicting the Next Word

After processing through these layers, the model calculates a probability distribution over its entire vocabulary for what the next word should be. The word with the highest probability is then selected as the prediction. For instance, given "The cat sat on the," the model might assign a 95% probability to "mat" and select it as the next word.

function TRANSFORMER_PREDICT_NEXT_WORD(sentence):
  1. Convert each word in 'sentence' to an embedding vector.
  2. For each word embedding:
     a. Calculate Query, Key, and Value vectors.
     b. Apply Multi-Headed Attention:
        i. For each attention head:
           1. Compute attention scores between Query and all Keys.
           2. Use scores to weight and combine Value vectors.
        ii. Concatenate and project outputs from all heads.
  3. Pass the context-rich word representations through multiple stacked layers.
  4. Apply a final linear layer and softmax to get probability distribution over vocabulary.
  5. Select the word with the highest probability as the prediction.
  6. Return predicted word.

Key takeaways

• Transformers process entire sentences at once, not word by word.
• Words are converted into numerical embeddings for mathematical analysis.
• Attention mechanisms link words by relevance to build context.
• Multi-headed attention allows simultaneous analysis of different relationships.
• Stacked layers deepen the model's understanding of text.
• The model predicts the next word by calculating probabilities based on learned context.