Understanding Transformer Architecture in Simple Terms

What Are Transformers?

Transformers are a type of neural network architecture named for their ability to “transform” how artificial intelligence (AI) processes sequences of data, especially text.

Introduced by Google researchers in their 2017 paper, Attention Is All You Need, Transformers significantly improved Natural Language Processing (NLP) tasks by using a mechanism called Self-Attention (Golroudbari).

Why the Name “Transformer”?

Transformers earned their name because they change how AI understands text sequences.
Traditional AI models handled text sequentially (word-by-word), leading to slower and less accurate processing.
Transformers, instead, analyze the entire text simultaneously, identifying relationships between words regardless of their position.

Key Innovation: Self-Attention Mechanism

Self-Attention allows the AI to identify and prioritize the most important words within a sentence, regardless of their position (Golroudbari).

Example:

Sentence: “The cat sat on the mat.”

The model understands that “cat” and “mat” are closely related, even if they are separated by other words. This ability makes understanding context and relationships more accurate and efficient.

Credit: https://github.com/jessevig/bertviz

How Transformers Work

Transformers operate in several steps:

Input Embedding: Words are converted into numerical representations.
Self-Attention: Identifies and prioritizes relevant words simultaneously.
Feed-Forward Layers: Processes and refines this information.
Output Generation: Produces meaningful results (such as responses or translations).

Why Are Transformers Important?

Speed: They process all words at once rather than sequentially.
Efficiency: Reduces computation time and complexity.
Accuracy: Improves understanding by better capturing context and word relationships.

Real-World Applications

Chatbots (e.g., ChatGPT)
Translation tools
AI content generation tools

Summary

Transformers fundamentally change how AI understands and processes language by using self-attention to efficiently capture relationships between words, making AI faster and more accurate in tasks such as translation, content creation, and chatbots.

Works Cited

Golroudbari, Arman Asgharpoor. “Understanding Self-Attention – A Step-by- Step Guide.” armanasq.github.io, armanasq.github.io/nlp/self-attention/. Accessed 17 Mar. 2025.