What is Chain of Thought Prompting?
Answer
Chain of Thought Prompting
How large language models reason through problems step by step.
🗓️ July 24, 2024
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 towards 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 achieve better results on reasoning-based tasks. A related variation, called zero-shot CoT prompting, includes the phrase “Let’s think step by step” to encourage the model to reason through a problem without prior examples[1].
Overall, Chain of Thought Prompting represents a major advancement in AI reasoning, allowing language models to perform at a higher cognitive level similar to human problem-solving[3].
📚 Sources
Source: Conversation with Copilot, 7/24/2024
- Chain-of-Thought Prompting | Prompt Engineering Guide
- Wei et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (arXiv:2201.11903)
- 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
Written by: Copilot
Formatted by: ChatGPT (GPT-5)
Edited by: Peter Z. McKay
July 24, 2024 Chain of Thought Prompting 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 towards 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), where they demonstrated that generating a chain of thought, which includes a series of intermediate reasoning steps, significantly improves the performance of LLMs on a range of arithmetic, commonsense, and symbolic reasoning tasks [2]. CoT prompting can be combined with few-shot prompting to get better results on tasks that require reasoning before responding. Additionally, a variation called zero-shot CoT prompting has been explored, where the prompt includes a phrase like "Let's think step by step" to encourage 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]. Source: Conversation with Copilot, 7/24/2024 Chain-of-Thought Prompting | Prompt Engineering Guide [2201.11903] Chain-of-Thought Prompting Elicits Reasoning in Large ... 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 https://doi.org/10.48550/arXiv.2201.11903