Let's Talk About Our Most Familiar Stranger: The Transformer (The "T" in GPT)

I bet it feels like a mysterious black hole to many—something you hear about but don't quite grasp. Today, I'll break down this crucial concept of LLMs (Large Language Models) in simple terms. Let's dive in!

The Transformer is a revolutionary deep learning model introduced in 2017 by Vaswani et al. Its key is the Self-Attention Mechanism, and it's specifically designed to handle sequential data, completely changing the game in Natural Language Processing (NLP).

Think of it as a "language translator" that doesn't just translate text but also generates articles, answers questions, and even holds conversations. That's why the Transformer is a dominant force in modern NLP tasks.

To understand it, let's start with the story of a super librarian.

Once upon a time, there was a magical library. This library had a super librarian who's our star today—the Transformer. This librarian had extraordinary abilities, quickly understanding and processing texts in various languages, answering questions, and even creating new content. Let's follow this librarian and explore how the Transformer works.

The Library and the Librarian

The Librarian's Journey (Training Process)

1.1 Apprenticeship: Massive Reading (Pre-training)

Our librarian wasn't born knowing all languages. They learned by constantly reading tons of books. Every time they tried to translate or answer a question, a machine tutor (training algorithm) and a human tutor (supervised fine-tuning) would tell them what they did right and where to improve. Through this constant practice and feedback, the librarian gradually improved their skills.

1.2 Professional Development: Specialized Training (Fine-tuning)

The librarian first gained broad knowledge by reading a large number of general books (pre-training). Later, if they needed to handle literature in a specific field, they would focus on reading books in that field to adjust their knowledge structure (fine-tuning).

1.3 The Librarian's Superpowers (Advantages of the Transformer)

Once the librarian completed their training, they gained the following superpowers:

• Parallel Processing (Self-Attention): The librarian could read all pages of a book simultaneously, making their reading speed incredibly fast.
• Multi-Head Attention: The librarian could capture information from different angles. It's like using a magnifying glass, a microscope, and a telescope to observe a flower at the same time, seeing textures, cells, and the surrounding environment.
• Long-Distance Relationships: They could easily connect information from the beginning and end of a book.
• Flexible Application: Whether it's translation, summarization, or Q&A, they could handle it all.

1.4 The Librarian's Troubles (Limitations of the Transformer)

• Memory Limit (Context Length): Despite their abilities, the librarian can only process a limited amount of text at a time. If a reader gives them more than 10 thick books at once (like a 1024-word limit), the librarian might miss the later content—that's why ChatGPT "forgets" previous topics in long conversations.
• Computational Resources: This reading method requires a lot of energy (GPU computing resources).
• Interpretability: Sometimes the librarian can't explain why they came to a specific conclusion (AI black box).
• Hallucinations: Sometimes, even with knowledge they haven't learned, they'll confidently talk nonsense (hallucinations).

1.5 The Structure of the Library (Overall Architecture of the Transformer)

Our super library is divided into two main parts:

Reading Room (Encoder): This is where the librarian reads and understands the input text.

Working Process:

1. Break the input text into word cards (Tokenize) → Split "I love machine learning" into four clue cards.
2. Mark the relationships with a highlighter (Self-Attention) → Find a strong connection between "learning" and "machine."
3. Add time labels (Positional Encoding) → Make sure it's "I → love → machine → learning" instead of the other way around, ensuring the correct order.

Real Example: When you enter "How tall is IFC?"

The encoder is like a detective:

1. Circle "IFC" (subject).
2. Link "how tall" with the numerical unit (verb-object structure).
3. Mark this as a "question" rather than a statement.

Writing Room (Decoder): This is where the librarian creates new content based on their understanding.

Working Process:

1. Refer to the librarian's report (Encoder output).
2. Gradually spell out reasonable word blocks (Auto-Regressive Generation) → First put "IFC," then choose "412 meters" instead of "50 floors."
3. Check fluency at any time (Masked Attention) → Avoid generating contradictory combinations like "412 kilograms."

Real Example:

1. Lock in "IFC" for a numerical answer (look at the encoder report).
2. Choose "height" instead of "weight" as the quantifier.
3. Align the unit "meters" with the value "412."

Final generation: Generates the answer "IFC is 412 meters tall."

These two rooms are closely connected, and the librarian can move between them at any time, just like the encoder and decoder parts of the Transformer work together.

This cross-room collaboration is the secret to the Transformer's ability to converse fluently!

1.6 Comparison with Other Libraries (Comparison with Other Models)

• Traditional Library (RNN): The librarian must read from beginning to end in order, without skipping.
• Improved Traditional Library (LSTM): The librarian can remember longer content but still needs to read in order.
• Super Library (Transformer): The librarian can see all the content at the same time and can freely focus on any part.

Okay, now everyone should understand the structure of this library and the librarian's abilities! But how does the librarian actually work? I will explain the librarian's work in detail in the next article, and we will explore how the real Transformer architecture works together.