Self-Harmonized Chain of Thought
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.
66e250292da5199123694af7
Ziqi Jin
claimed_verified
2024-09-19T18:38:58.876Z
625921d05f80a3c1aad0bae3
https://cdn-avatars.huggingface.co/v1/production/uploads/625921d05f80a3c1aad0bae3/ElN3-6V5nGId2fzI3Dqlr.jpeg
Phi
user
Xalphinions
66e250292da5199123694af8
Wei Lu
Self-Harmonized Chain of Thought
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.
ECHO is a self-harmonized chain-of-thought prompting method that improves reasoning performance by consolidating diverse solution paths into a uniform pattern.
Chain-of-Thought (CoT) prompting large language models reasoning intermediate steps step-by-step demonstrations self-harmonized solution paths solution pattern