自我协调的思维链

Self-Harmonized Chain of Thought

September 6, 2024

作者: Ziqi Jin, Wei Lu

cs.AI

摘要

思维链（Chain-of-Thought，CoT）提示显示，大型语言模型能够通过中间步骤进行复杂推理。CoT提示主要分为三种方法。第一种方法使用直接提示，如“让我们逐步思考”，以在给出答案之前生成顺序思维过程。第二种方法利用人工制作的逐步演示来引导模型的推理过程。第三种方法自动生成推理演示，采用“让我们逐步思考”。这种方法有时会导致推理错误，突显了多样化演示以减轻其误导效果的必要性。然而，多样化演示对于有效表示提出了挑战。在这项工作中，我们提出了ECHO，一种自我协调的思维链提示方法。它将多样的解决路径整合为统一且有效的解决方案模式。ECHO在三个推理领域中展示了最佳的整体性能。

English

Chain-of-Thought (CoT) prompting reveals that large language models are capable of performing complex reasoning via intermediate steps. CoT prompting is primarily categorized into three approaches. The first approach utilizes straightforward prompts like ``Let's think step by step'' to generate a sequential thought process before yielding an answer. The second approach makes use of human-crafted, step-by-step demonstrations to guide the model's reasoning process. The third automates the generation of reasoned demonstrations with the 'Let's think step by step'.This approach sometimes leads to reasoning errors, highlighting the need to diversify demonstrations to mitigate its misleading effects. However, diverse demonstrations pose challenges for effective representations. In this work, we propose ECHO, a self-harmonized chain-of-thought prompting method. It consolidates diverse solution paths into a uniform and effective solution pattern.ECHO demonstrates the best overall performance across three reasoning domains.