Explain the Transformer to a Smart Freshman

Explaining the transformer model in plain English to a smart college freshman isn’t easy, especially when he asks for everything during office hours. So this post takes on this challenge. The questions in this post may be something that you are also concerned.

Just in case you do not know. The Transformer model, introduced in 2017, is the foundational framework that revolutionized natural language processing (NLP) for various tasks such as machine translation, text summarization, and language generation. It enabled the development of many large-scale language models like GPT-X or the recent hot topic DeepSeek.

A Transformer has many essential components (Doesn’t your coffee maker have many parts as well) as listed below. These components work together to enable the Transformer model to effectively process and generate sequences.

• Intake: Tokenization
• Embedding
• Positional Encoding
• Self-Attention Mechanism
• Multi-Head Attention
• Feed-Forward Neural Networks
• Encoder
• Decoder
• Residual Connections and Layer Normalization (Add & Norm)