1. Let’s first talk about what problems they solve
2. Let’s talk about what the structure looks like
3. Let’s talk about how to implement it in Transformer
4. Finally, let’s talk about which parts of BERT and GPT are used respectively.

1. Why do we need Encoder and Decoder?

When doing "machine translation" in the early days, researchers faced a problem:

How to change one sentence into another sentence?

For example:

Chinese:

I like learning artificial intelligence

English:

I like studying artificial intelligence

This is actually a two-step process:

1️⃣ First “understand” the original sentence 2️⃣ Then "generate" the target sentence

So there is:

Encoder → 负责理解
Decoder → 负责生成

2. What is Encoder?

✅ Encoder = understanding module

Its mission is:

Turn the input sentence into a set of "semantic representations"

For example, enter:

我 喜 欢 学 习

Encoder will output:

[向量1] [向量2] [向量3] [向量4]

These vectors are not ordinary numbers.

They are:

Semantic encoding containing contextual information

means:

• "Like" will know that the subject is "I"
• "Learning" will know that the preceding word is "like"

Features of Encoder

• Enter all sentences
• Every word can see all words (Self-Attention)
• Output a set of "context enhancement vectors"

Simply put:

Encoder is responsible for "understanding the entire sentence"

3. What is Decoder?

✅ Decoder = Generate module

Its mission is:

Generate output word by word based on existing information

For example, when translating:

The first step generates:

I

The second step generates:

I like

The third step generates:

I like studying

Keep generating until the end of the sentence.

Features of Decoder

• Only words that have been generated previously can be seen
• Can't see the future
• Predict the next word at each step

This is called:

Auto-regressive generation (Auto-regressive)

4. Encoder/Decoder in Transformer

Representative papers:

Attention Is All You Need

The original structure of Transformer is:

Encoder 堆叠 N 层
Decoder 堆叠 N 层

Each layer of Encoder contains:

• 1️⃣ Self-Attention
• 2️⃣ Feedforward neural network

Function:

Establish relationships between words

Each layer of Decoder contains:

• 1️⃣ Masked Self-Attention (can only see the front)
• 2️⃣ Cross-Attention (see Encoder output)
• 3️⃣ Feedforward neural network

Here comes the point:

What is Cross-Attention?

When the Decoder is generated, it will:

• Look at the content you have generated
• Also refer to the "Understanding Results" given by Encoder

So:

Encoder provides understanding Decoder is responsible for expressing

5. Three common structural types

Now we explain the three variations.

1️⃣ Encoder-only model

Representative:

BERT: Pre-training of Deep Bidirectional Transformers

Structure:

只用 Encoder

Purpose:

• Text classification
• sentiment analysis
• Search sorting

Why?

Because it only requires:

Understand, no need to generate

2️⃣ Decoder-only model

Representative:

GPT-4 From: OpenAI

Structure:

只用 Decoder

Why?

Because it treats "input content" as generated content.

For example:

Input:

问题：什么是AI？
回答：

Then start generating.

Its essence is:

Always predicting the next word

So GPT does not require an Encoder.

3️⃣ Encoder-Decoder model

Representative:

T5

From: Google

Structure:

完整 Transformer

Commonly used for:

• Translate
• Summary
• Complex text conversion

6. An intuitive analogy

Imagine you hire a translator:

Encoder is:

After listening to Chinese, I completely understand the meaning.

Decoder is:

Re-express it in English.

If it is BERT:

There are only "understanders".

If it's GPT:

There are only "writers".

If it is T5:

Write after listening.

7. Let’s talk about another key point: Why does GPT not need an Encoder?

Because what GPT does is:

conditional language generation

The input content is already in front.

For example:

翻译：我爱你 →

The model only needs to predict:

I love you

So:

The input itself is equivalent to "implicit encoding"

8. Summarize in one sentence

Encoder：

Responsible for understanding global semantics

Decoder：

Responsible for generating text in sequence

BERT：

only understand

GPT：

Only generate

T5：

Understand + generate