Introduction to Chain-of-Thought
Chain-of-Thought (CoT) is a prompt-engineering technique that improves the reasoning capabilities of large language models (LLMs) by prompting them to generate a sequence of intermediate reasoning steps.
How CoT Works
Instead of asking for a direct answer, you prompt the model to “think step by step” or provide a few examples of problems with their intermediate steps.
Normal Prompt: “What is the sum of all prime numbers between 1 and 10?”
CoT Prompt: “Think step by step to find the sum of all prime numbers between 1 and 10.”
- The prime numbers between 1 and 10 are 2, 3, 5, and 7.
- The sum is 2 + 3 + 5 + 7 = 17.
- Therefore, the sum is 17.
Benefits of CoT
- Better Accuracy: LLMs can solve complex mathematical or logical problems more accurately by breaking them down into simpler steps.
- Explainability: You can see how the model arrived at the final answer.
- Few-Shot CoT: Using examples of reasoning in the prompt can significantly boost the model’s performance on similar tasks.
Variations of CoT
- Self-Consistency: Generating multiple reasoning paths and selecting the most frequent answer.
- Tree-of-Thoughts: Exploring multiple branches of reasoning to find the best solution.
- Least-to-Most Prompting: Breaking a large problem into smaller subproblems and solving them sequentially.