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Chain of Thought Prompting

How Large Language Models Learn to Reason Step-by-Step

Chain of Thought Prompting (CoT) is a technique in artificial intelligence that enhances the reasoning capabilities of large language models (LLMs). It involves breaking down complex tasks into a sequence of logical steps toward a final resolution.^[1][2][3] This method simulates human-like reasoning processes by providing a structured mechanism for problem-solving.^[3]

For example, when solving a math problem, CoT prompting would guide the model to articulate each step of the calculation process rather than just providing the final answer. This approach helps LLMs tackle more complex reasoning tasks that require multiple steps to solve.^[1]

The technique was introduced in a paper by Wei et al. (2022), which demonstrated that generating a chain of thought — a series of intermediate reasoning steps — significantly improves the performance of LLMs on arithmetic, commonsense, and symbolic reasoning tasks.^[2]

CoT prompting can be combined with few-shot prompting to get better results on tasks requiring reasoning before responding. Additionally, a variation called zero-shot CoT prompting has been explored, where the prompt includes a phrase such as “Let’s think step by step.” This encourages the model to reason through the problem without prior examples.^[1]

Overall, Chain of Thought Prompting represents a significant advancement in the field of AI, enabling language models to perform at a higher level of cognitive function akin to human problem-solving.^[3]

🔗 Sources

• Conversation with Copilot, July 24 2024.
• [2201.11903] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022)
• Chain-of-Thought Prompting | Prompt Engineering Guide
• What is Chain of Thoughts (CoT)? | IBM
• Master Prompting Concepts: Chain of Thought Prompting – Prompt Engineering
• Chain of Thought Prompting: Guiding LLMs Step-by-Step