Big Models Equal Small Town Test-Takers: Explaining LLM in Simple Language

When we marvel at ChatGPT's fluent answering wisdom, have you ever thought about the learning process of this "student master"? It is actually very similar to a small town test-taker who studies hard - repeatedly training in a vast question bank, "chewing textbooks" through pre-training, and then "brushing five-three simulations" in the fine-tuning stage, and finally passing the "college entrance examination" in the digital world. But what kind of amazing technical truth is hidden behind this figurative metaphor?

Pre-training: AI’s “nine-year compulsory education”

If we compare the AI ​​model to a student, the pre-training phase is the entire process of its basic education. This process is like "feeding" the machine with the world's knowledge (including the entire Internet) - from the Four Books and Five Classics to online jokes, from scientific research documents to recipe guides, the average daily "reading volume" is equivalent to the knowledge accumulated by humans for thousands of years.

Three magic weapons for building a "digital brain"

Data food: Swallowing terabytes of text every day (equivalent to millions of novels), and forming knowledge reserves through cleaning and filtering

Neural Network Classroom: Transformer architecture is like a special teacher, using the "self-attention" mechanism to teach AI to understand contextual relationships

Unsupervised self-learning: Cover part of the text and let AI fill in the blanks (MLM task), training the reasoning ability of "seeing the beginning and knowing the end"

This process is like letting AI practice in a library. When it can connect "The sun sets behind the mountains" to "The Yellow River flows into the sea", it has completed the basic cognitive construction. But at this time, AI is like a junior high school graduate who has a lot of knowledge but doesn't know how to use it.

If AI just memorizes all the knowledge points, can it be considered to truly "understand" the knowledge? Leave a message in the comment section to discuss.

Fine-tuning: "Experts in Problem Solving" Created by the Sea of ​​Questions Strategy

When the basic model has a knowledge reserve of 70 points, engineers begin to implement "devil training" - through supervised learning and reinforcement learning, they turn "nerds" into "exam experts".

Supervised learning: one-on-one tutoring by famous teachers

Manually annotated tens of thousands of high-quality questions and answers (such as "Who is the author of "Quiet Night Thoughts"? → Li Bai")

The model learns to standardize problem-solving ideas by correcting wrong answers

This process is similar to a teacher marking homework, marking errors with a red pen.

Reinforcement Learning: Mock Exam Sprint Training

Build a reward model (RM) as a "grading teacher" to score AI answers

Let AI generate 10 answer variations, select the highest-scoring version and iterate and optimize

Just like students familiarize themselves with the grading criteria through mock exams and figure out the "scoring points"

After this "sea of ​​questions" strategy, the accuracy of AI's answers can be increased from 70% to 90%+. But what is shocking is that this process consumes enough electricity to power a small city for a year, and the labeling cost is as high as millions.

Technology Mirror: The "Innate Deficiencies" of AI Scholars

Although the big model can get high scores in the college entrance examination essay, it is still essentially an "advanced repeater" (a parrot). Some cruel truths:

Knowledge timeliness : After the training data is cut off, AI is "ignorant" of news events

Logical weakness: It is easy to make mistakes when faced with the trap question "Which is heavier, 10 kilograms of iron or 10 kilograms of cotton?"

Value risk: dangerous content with training data bias may be output

Energy black hole: The carbon emissions from training GPT-3 are equivalent to the lifetime emissions of 5 cars

When AI is better at taking exams than humans, will the essence of education be alienated?

Outlook: From "Test-taker" to "Creator"

The current large-scale model has shown amazing potential:

Legal AI: Generate professional indictments in 3 seconds with an accuracy rate of over 90%

Medical assistants: interpret CT images 100 times faster than doctors

Coding mentors: can find code vulnerabilities that human programmers overlook

However, in order for AI to break through the limitations of "test solvers", it still needs to break through:

Multimodal fusion: Let AI understand drawings and dialects

Continuous learning: Establish a dynamic knowledge updating mechanism

Super Alignment: Building an Ethical "Digital Lifestyle"

Symbiosis between humans and AI

The development trajectory of the AI ​​big model is just like a microcosm of the Chinese education system - transforming "knowledge containers" into "problem-solving tools" through systematic training. But the true meaning of education is not to cultivate a perfect answering machine, but to inspire the spark of wisdom. When AI is invincible in digital examination halls, humans need to protect the originality of ideas and the warmth of the soul. This dance between humans and AI may be the most worthwhile chapter to write in the intelligent era.