Many people will be confused:

Doesn’t GPT “only predict the next word”? So why can it "understand the problem"?

1. Let’s talk about the core conclusions first

GPT is not a matter of "really understanding" it.

What it does is:

Learned from large amounts of training data When seeing some kind of problem structure What answer structure should be generated.

In other words:

It learns the statistical mapping of "question → reasonable answer".

But there is a very deep mechanism behind this.

2. GPT’s training method determines everything

Take GPT-3 and GPT-4 as examples (from OpenAI).

There is only one thing they do when training:

预测下一个词

For example, give it:

Earth revolves around the sun ____

It wants to predict:

run

The training data size is:

• • Hundreds of billions of words
• • From books, web pages, papers, conversations

In the process, it learned:

• • what is the problem
• • what is the answer
• • what is explanation
• • what is logical reasoning
• • what are mathematical steps

3. Why does GPT “seem to understand the problem”?

Because of three things.

① Transformer’s Attention mechanism

GPT is based on Transformer (paper: Attention Is All You Need).

Attention’s core capabilities are:

Each word can focus on all previous words.

When you ask:

Why is the sky blue?

When the model generates an answer:

• • Will focus on “why”
• • Pay attention to "Sky"
• • Follow "Blue"

Through attention weights, it captures:

• • This is a cause and effect question
• • Need to explain the principle
• • Don't just give a definition

So it's not a wild guess.

It is modeling the "problem structure".

② Pattern learning brought about by large-scale pre-training

During training, it saw countless texts like this:

问：为什么天空是蓝色？
答：因为光的瑞利散射……

Over time, it learns:

• • “Why” is usually followed by a causal explanation
• • “What is” is usually followed by a definition
• • “How to” is usually followed by steps

It’s not about memorizing an answer.

Instead learn to:

Structural regularities in language.

③ Representation Learning

This is the most critical point.

In Transformer, each word is turned into a high-dimensional vector.

This vector is not a simple "word number".

It will encode:

• • meaning
• • grammatical role
• • context
• • emotions
• • Logical relationship

When you ask a question:

A "semantic space state" will be formed inside the model.

This status already contains:

• • What type of question are you asking?
• • What type of answer is needed?
• • what tone should be used

It's not a word-for-word match.

Rather, it enters a "semantic state".

4. Give a specific example

You asked:

What is Newton's second law?

What happens inside the model:

• 1. Identify “what” → definitional question
• 2. Identify “Newton’s Second Law” → Physics Concept
• 3. Activate relevant knowledge representation
• 4. Generate definition sentences

So the output:

Newton's second law states that the acceleration of an object is directly proportional to the net external force and inversely proportional to the mass...

It looks like "understanding".

But the essence is:

Predicting the "most reasonable next word" in semantic space.

5. Why can GPT be understood without Encoder?

Because GPT is a Decoder-only model.

But its Self-Attention allows:

当前词可以看到前面所有词

When the input is:

Question: Why is sea water salty?

When generating the answer:

You can see the complete problem at every step.

So although it is a "generative model",

But the question has been "read and encoded" before being generated.

This is equivalent to:

Comprehension is done dynamically during the generation process.

6. Does GPT really “understand” it?

This is a philosophical question.

From an engineering perspective:

It has no consciousness.

But from a functional perspective:

It has learned:

• • causal structure
• • mathematical rules
• • program logic
• • verbal reasoning

And these abilities emerge naturally from "predicting the next word."

This is called:

Emergent Ability

7. Use a simple analogy

Imagine a person:

• • Read all the books in the world
• • Viewed all Q&A
• • Remember all expression patterns
• • Learn all reasoning structures

When you ask him a question,

He may not really "understand the universe";

But he knows:

What is the most reasonable answer in this context.

This is GPT.

8. Summarize one sentence

The reason why GPT “understands the problem” is because:

• 1. The Attention mechanism allows it to model contextual relationships
• 2. Massive training allows it to learn the question-answer structure
• 3. High-dimensional semantic vectors allow it to form an abstract representation

Essentially:

It's not "understanding the problem"; It is "predicting a reasonable answer."

But when the scale is large enough,

This ability to predict will appear like understanding.