自我協調的思維鏈

Self-Harmonized Chain of Thought

September 6, 2024

作者: Ziqi Jin, Wei Lu

cs.AI

摘要

Chain-of-Thought (CoT)提示揭示了大型語言模型能夠通過中間步驟執行複雜推理的能力。CoT提示主要分為三種方法。第一種方法使用直接的提示，如“讓我們一步一步思考”，以在給出答案之前生成一個順序思考過程。第二種方法利用人工製作的逐步演示來引導模型的推理過程。第三種方法自動生成推理演示，使用“讓我們一步一步思考”。這種方法有時會導致推理錯誤，突顯了多樣化演示以減輕其誤導效應的必要性。然而，多樣化的演示對於有效的表示提出了挑戰。在這項工作中，我們提出了ECHO，一種自我協調的Chain-of-Thought提示方法。它將多樣的解決方案路徑整合為統一且有效的解決方案模式。ECHO在三個推理領域中展示了最佳的整體表現。

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.