How to Use LLaMA 3's Chain of Thought to Achieve Frequency Enhancement?(LLaMA 3 Practical 7)
Explore the limitations of LLaMA 3 in multi-round reasoning and how Self-Consistency improves reasoning accuracy. Learn effective strategies for complex tasks.
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Welcome to the "LLaMA 3 Practical" Series
In the previous lesson, we learned how to build a simple ChatGPT model, which laid the foundation for our subsequent learning.
You have already mastered the basic principles of generative pre-trained models and understood how to apply these principles to implement dialogue systems like ChatGPT.
In this lesson, we will continue to delve deeper into the limitations of the LLaMA 3 model when performing multi-round reasoning and explore how to effectively address these challenges.
In the fourth lesson, we took a detailed look at the Chain of Thought (CoT)-based multi-step reasoning approach. This method helps us break down complex problems into smaller, more manageable subproblems and incrementally advance through the reasoning process, enabling us to solve complex reasoning tasks more effectively.
Although Chain of Thought has its advantages in improving reasoning accuracy, it also exposes some limitations when handling multi-round reasoning tasks.
Limitations of LLaMA 3 in Multi-Round Reasoning
The LLaMA 3 model generates text based on an autoregressive generation process, predicting the next character or word to gradually construct a complete sentence or paragraph.
This method generally performs well, producing smooth and coherent text.
However, when handling complex, multi-step reasoning tasks, the model may exhibit inconsistent performance. In multiple attempts, the model may give different answers—sometimes correct, but other times incorrect.