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LLMsID: cot

Chain-of-Thought

思维链 CoT
🎯Core Definition
CoT (Chain-of-Thought) prompting: include intermediate reasoning steps in examples so the model decomposes complex problems into stepwise reasoning instead of jumping to the answer. The few-shot form shows reasoning chains in 2-8 examples; the zero-shot form appends "Let's think step by step" to elicit stepwise reasoning. Key empirical fact: CoT ability emerges markedly only at ~100B\geq 100B parameters; small models gain little.
💡Use Cases
math, logic, and multi-step reasoning; combined with self-consistency (sample multiple CoT paths and majority-vote the answer) it boosts accuracy further; also the foundational idea behind o1-style reasoning models.
Key Problems Solved
direct answer generation is inaccurate on hard tasks and errors can't be localized; CoT converts one big step into many small ones, giving the model explicit scratch space, reducing error propagation, and making reasoning inspectable and debuggable.
🎯5 High-Frequency Exam Points
1
Few-shot vs zero-shot CoT? Why does "Let's think step by step" work?
2
How does CoT emergence relate to model size, and why do small models benefit little?
3
How does self-consistency work, and why does majority voting improve accuracy?
4
Limitations of CoT? When is it ineffective or even harmful?
5
How does CoT relate to test-time compute scaling?
📖 In-depth Guide:📄 reasoning-and-cot
Updated 2026-08-12
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