The Path of Thought in LLaMA 3: The Origins and Applications of Chain-of-Thought(LLaMA 3 Practical 4)

Explore Chain of Thought (CoT): A step-by-step reasoning method that enhances large model accuracy for complex tasks.

Welcome to the "LLaMA 3 Practical" Series

Table of Contents

In the previous lesson, we discussed how to liberate large models from the traditional "dialog box" application scenarios, expanding their capabilities in practical applications through the use of instructions and tools.

However, even so, large models still face difficulties when dealing with complex tasks. When confronted with lengthy and intricate task descriptions, the model often struggles to grasp the key points, and may even make strange mistakes, which is clearly unacceptable in real-world applications.

So, how can we make large models more adept at handling complex problems?

Through continuous exploration, researchers have proposed a technique called Chain of Thought (CoT).

It not only helps the model better understand complex issues but also enables it to gradually reason through to an accurate answer.

Today, I will take you through an in-depth exploration of the Chain of Thought concept, its applications, technical implementation, and its potential in future intelligent agent applications.