Self-Harmonized Chain of Thought
Authors: name: Ziqi Jin, name: Wei Lu

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.