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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Wei, Wang, Schuurmans et al. · 2022 · NeurIPS 2022

PromptingRead on arXiv

Showed that prompting models with intermediate reasoning steps dramatically improves performance on reasoning-heavy tasks. Chain-of-thought is now a fundamental technique in prompt engineering.

Key Idea

Simply adding intermediate reasoning steps to few-shot examples causes LLMs to generate their own step-by-step reasoning, leading to dramatically better performance on math, logic, and commonsense reasoning tasks.

How It Works

  • Standard prompting: Q: "Roger has 5 tennis balls..." → A: "11"
  • Chain-of-thought prompting: Q: "Roger has 5 tennis balls..." → A: "Roger started with 5 balls. He bought 2 cans of 3 balls each, so 2 × 3 = 6 new balls. 5 + 6 = 11. The answer is 11."
  • The model learns to decompose problems by following the demonstrated reasoning pattern

Key Findings

  • CoT is an emergent ability: it only helps models above ~100B parameters. Smaller models produce incoherent reasoning chains that hurt performance
  • Improvements are largest on complex reasoning tasks (arithmetic, commonsense, symbolic)
  • Zero-shot CoT also works: simply adding "Let's think step by step" to the prompt triggers reasoning chains (Kojima et al., 2022)

Why It Matters

  • Unlocked a class of tasks that LLMs previously couldn't solve (multi-step math, logic puzzles)
  • Established prompting as a first-class research area alongside model architecture and training
  • Led to Tree-of-Thought, Graph-of-Thought, and other structured reasoning extensions
  • Now standard practice in all serious LLM applications

Key Takeaways for Interviews

  • CoT works by eliciting step-by-step reasoning through demonstration
  • It's emergent, only effective at large model scales
  • Zero-shot CoT ("Let's think step by step") is surprisingly effective
  • CoT improves accuracy but increases token usage (cost/latency tradeoff)