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