What is Chain of Thought Prompting?
Answer
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