What is Chain-of-Thought?
Chain-of-Thought (CoT) is a prompting technique that encourages large language models to break down complex reasoning tasks into intermediate steps, mimicking human thought processes to improve accuracy and explainability.
Quick Facts
| Full Name | Chain-of-Thought Prompting |
|---|---|
| Created | 2022 by Google Research |
| Specification | Official Specification |
How It Works
Chain-of-Thought prompting was introduced by Google researchers in 2022 as a method to enhance the reasoning capabilities of large language models. By explicitly asking models to show their work step by step, CoT enables better performance on arithmetic, commonsense, and symbolic reasoning tasks. This technique has become fundamental in prompt engineering for complex problem-solving.
Key Characteristics
- Step-by-step reasoning that breaks complex problems into manageable parts
- Improved accuracy on mathematical and logical tasks
- Enhanced explainability through visible reasoning process
- Zero-shot and few-shot variants available
- Works best with larger language models (100B+ parameters)
- Can be combined with other prompting techniques like self-consistency
Common Use Cases
- Mathematical problem solving and arithmetic reasoning
- Multi-step logical deduction tasks
- Complex question answering requiring inference
- Code debugging and algorithm explanation
- Scientific reasoning and hypothesis testing
Example
Loading code...Frequently Asked Questions
What is the difference between zero-shot and few-shot chain-of-thought?
Zero-shot CoT uses phrases like 'Let's think step by step' without examples. Few-shot CoT provides examples of step-by-step reasoning for the model to follow. Few-shot typically produces better results but requires more prompt engineering.
Why does chain-of-thought improve model accuracy?
CoT improves accuracy by breaking complex problems into simpler intermediate steps, allowing the model to allocate more computation to each step. This mimics how humans solve problems and reduces errors in multi-step reasoning.
Does chain-of-thought work with all language models?
CoT works best with larger models (typically 100B+ parameters). Smaller models may not benefit significantly or may produce incorrect reasoning steps. The technique emerged with the scaling of language models.
What types of problems benefit most from chain-of-thought?
CoT is most effective for arithmetic word problems, multi-step logical reasoning, commonsense reasoning, and tasks requiring inference. It is less beneficial for simple factual recall or classification tasks.
Can chain-of-thought be combined with other prompting techniques?
Yes, CoT can be combined with self-consistency (sampling multiple reasoning paths), tree-of-thought (exploring multiple branches), and other techniques to further improve reasoning accuracy and reliability.