How transformers work: Álvaro García Pizarro

How Do Transformers Work in AI?

In recent years, Transformers have revolutionized the field of artificial intelligence, especially in tasks like natural language processing (NLP) and computer vision. Introduced in the paper “Attention is All You Need” (Vaswani et al., 2017), Transformers have surpassed traditional approaches like recurrent neural networks (RNNs) and convolutional neural networks (CNNs) thanks to their ability to handle large amounts of data and context efficiently. Here’s how they work.

1. What Are Transformers?

A Transformer is a type of neural network designed to process sequential data. Its key feature is the Attention mechanism, which allows the model to focus on the most relevant parts of a sequence, regardless of its length.

This model serves as the foundation for technologies like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers).

2. Key Components

The Transformer is divided into two main blocks:

1. Encoder: Processes the input and generates abstract representations of the data.

2. Decoder: Uses these representations to produce the desired output.

For tasks like text generation (e.g., GPT), only the decoder is used. For tasks like classification or sentiment analysis, only the encoder is required.

Basic Structure

Each block (Encoder and Decoder) contains:

• Multi-Head Attention: An extension of the attention mechanism that allows the model to focus on different parts of the sequence simultaneously.

• Feed-Forward Neural Network: A fully connected network that transforms representations after applying attention.

• Normalization & Residual Connections: Stabilize training and improve convergence.

3. How Does the Attention Mechanism Work?

The core of the Transformer is Scaled Dot-Product Attention, which is based on three matrices derived from the input data:

• Query (Q): Represents the element seeking relevant information.

• Key (K) and Value (V): Contain the sequence’s information.

The main calculation is:



• : Computes the similarity between queries and keys.

• : Scaling factor to prevent gradient explosion.

• Softmax: Converts values into probabilities.

• : Provides values associated with the most relevant keys.

4. How Are Data Processed?

1. Positional Encoding: Since Transformers lack sequential memory (unlike RNNs), positional information is added so the model can understand word order.

2. Training: The model is trained on large datasets using techniques like backpropagation and modern optimizers like Adam.

3. Parallelization: Transformers process entire sequences simultaneously (not step-by-step), making them significantly faster than RNNs.

5. Applications of Transformers

• Natural Language Processing:

• Machine translation.

• Text summarization.

• Content generation (e.g., ChatGPT).

• Computer Vision:

• Image classification with models like Vision Transformers (ViT).

• Computational Biology:

• Protein structure prediction.

6. Practical Example

Imagine training a Transformer to translate “I love AI” into Spanish. The process would be:

1. The text is converted into numerical vectors, with positional encoding added.

2. The encoder analyzes each word and generates representations based on their relationships.

3. The decoder uses these representations to produce the translation, word by word, starting with “Yo,” followed by “amo” and “la IA.”

Conclusion

Transformers have changed how we tackle AI problems. Their ability to handle large-scale context, combined with computational efficiency, makes them an essential tool in the era of large language models and beyond. With continued advancements in hardware and training techniques, the future of Transformers looks even brighter.

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