Chain of Thought

Chain of Thought (CoT) is a technique for enhancing logical reasoning in large language models. This concept was first proposed by Jason Wei, a senior researcher at Google Brain, and published in a paper titled “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" CoT technology decomposes complex problems into a series of step-by-step sub-problem answers, guiding the model to generate a detailed reasoning process, thereby improving the model's performance on complex tasks such as arithmetic reasoning, common sense reasoning, and symbolic reasoning.

The key advantage of CoT technology is that it can significantly improve the interpretability of the model and help the model perform complex logical reasoning, especially in problems that require combining multiple facts or pieces of information. It imitates the human reasoning process, which is usually not to get the answer directly, but through a series of thinking, analysis, and reasoning steps. The CoT method is divided into two forms: Few-shot CoT based on manual example annotation and Zero-shot CoT without manual example annotation. Few-shot CoT demonstrates the reasoning process by providing a small number of examples, while Zero-shot CoT stimulates the model to generate reasoning chains through specific prompts (such as "Let's think step by step") without the need for additional examples.