Chain-of-thought (CoT)  prompting reveals that large language models are capable of performing complex reasoning via intermediate steps.
CoT prompting is mainly categorized into two paradigms.
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 latter method replaces manually written demonstrations with ones generated by large language models using “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.
Consolidating diverse solution paths into a uniform and effective solution pattern,
ECHO demonstrates the best overall performance in three reasoning domains111Our code is available:https://github.com/Xalp/ECHO.