Chain-of-Thought Prompting in Reasoning

Chain-of-Thought (CoT) Prompting

• Enables complex reasoning capabilities through intermediate reasoning steps
• Can be combined with few-shot prompting to achieve better results on complex tasks that require reasoning

Examples of CoT Prompting

• Adding all the odd numbers (9, 15, 1) gives 25 → The answer is False
• Adding all the odd numbers (17, 19) gives 36 → The answer is True
• Adding all the odd numbers (11, 13) gives 24 → The answer is True
• Adding all the odd numbers (17, 9, 13) gives 39 → The answer is False
• Adding all the odd numbers (15, 5, 13, 7, 1) gives 41 → The answer is False

Zero-Shot CoT

• Introduced by Kojima et al. (2022)
• Involves adding "Let's think step by step" to the original prompt
• Example: I went to the market and bought 10 apples... → Let's think step by step → Then you bought 5 more apples, so now you had 11 apples. Finally, you ate 1 apple, so you would remain with 10 apples.

Auto-CoT

• Proposed by Zhang et al. (2022)
• Eliminates manual efforts by leveraging LLMs with "Let's think step by step" prompt to generate reasoning chains for demonstrations one by one
• Consists of two main stages:
  • Sampling questions with diversity
  • Generating reasoning chains to construct demonstrations
• Encourages the model to use simple and accurate demonstrations by using simple heuristics such as:
  • Length of questions (e.g., 60 tokens)
  • Number of steps in rationale (e.g., 5 reasoning steps)

Quiz on Chain-of-Thought prompting, a technique that enables complex reasoning capabilities through intermediate reasoning steps.