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Chain-of-Thought prompting aims to improve the reasoning ability of language models. It guides the model to show its reasoning process step by step in the prompt instead of just giving the final answer. For example, for a math problem, the model may be prompted to first explain its understanding of the problem, then list the steps to solve the problem, and finally give the answer.
This method can help improve the interpretability of the model's output, allowing users to better understand how the model reaches its conclusion. At the same time, chain-of-thought prompting can also help improve the model's performance on complex reasoning tasks because it prompts the model to think about problems more systematically.
Chain-of-thought prompting can be combined with other prompt engineering techniques to further enhance the capabilities of language models. For example, it can be combined with few-shot prompting to provide the model with some examples with chain-of-thought, helping the model better understand the task requirements.
In general, "Reasoning and Logic Chain-of-Thought (CoT) Prompting" plays an important role in improving the reasoning ability and interpretability of large language models and is a key technical direction in the field of prompt engineering.
In 2022, Chain-of-Thought (CoT) prompting was introduced as a technique for prompting large language models (LLMs). It facilitates coherent and step-by-step reasoning processes. The authors demonstrated its effectiveness through experiments, showing that it elicits more structured and thoughtful responses. For example, it shows the reasoning process for math word problems. The authors achieved state-of-the-art performance with PaLM 540B using CoT prompts, with an accuracy of 90.2%.
Chain-of-Thought (CoT) prompting has several main differences from traditional prompts:
Traditional prompts usually just give a task description and may not explicitly guide the model through detailed reasoning steps. For example, for a math problem, a traditional prompt might simply state the problem itself without involving any guidance on the reasoning process. However, CoT prompting explicitly guides the model through a coherent and step-by-step reasoning process. For instance, when dealing with a multi-step math problem, it shows the reasoning process and final answer, mimicking how humans break down problems into logical intermediate steps, thus prompting the model's response to reflect a deeper understanding of the given prompt.
Under traditional prompts, the model's responses may be relatively simple and direct, lacking structured thinking and detailed reasoning display. CoT prompting can prompt the model to generate more structured and thoughtful responses. By showing the reasoning chain, the model's answers are more organized, helping to improve the accuracy and interpretability of the answers.