title: Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning
    by Large Language Models
summary: Large language models (LLMs) have recently been shown to deliver impressive
  performance in various NLP tasks. To tackle multi-step reasoning tasks,
  few-shot chain-of-thought (CoT) prompting includes a few manually crafted
  step-by-step reasoning demonstrations which enable LLMs to explicitly generate
  reasoning steps and improve their reasoning task accuracy. To eliminate the
  manual effort, Zero-shot-CoT concatenates the target problem statement with
  "Let's think step by step" as an input prompt to LLMs. Despite the success of
  Zero-shot-CoT, it still suffers from three pitfalls: calculation errors,
  missing-step errors, and semantic misunderstanding errors. To address the
  missing-step errors, we propose Plan-and-Solve (PS) Prompting. It consists of
  two components: first, devising a plan to divide the entire task into smaller
  subtasks, and then carrying out the subtasks according to the plan. To address
  the calculation errors and improve the quality of generated reasoning steps, we
  extend PS prompting with more detailed instructions and derive PS+ prompting.
  We evaluate our proposed prompting strategy on ten datasets across three
  reasoning problems. The experimental results over GPT-3 show that our proposed
  zero-shot prompting consistently outperforms Zero-shot-CoT across all datasets
  by a large margin, is comparable to or exceeds Zero-shot-Program-of-Thought
  Prompting, and has comparable performance with 8-shot CoT prompting on the math
  reasoning problem. The code can be found at
  https://github.com/AGI-Edgerunners/Plan-and-Solve-Prompting.
paper_authors._id: 6459d787130a95cb18fe52bc
paper_authors.name: Lei Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-24T15:31:13.452Z
paper_authors._id: 646def60df618b303b419323
paper_authors.avatarUrl: /avatars/97aa761d5255abf230304cfeade87835.svg
paper_authors.fullname: Lei Wang
paper_authors.type: user
paper_authors.user: demolei
paper_authors._id: 6459d787130a95cb18fe52bd
paper_authors.name: Wanyu Xu
paper_authors._id: 6459d787130a95cb18fe52be
paper_authors.name: Yihuai Lan
paper_authors._id: 6459d787130a95cb18fe52bf
paper_authors.name: Zhiqiang Hu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-27T09:48:21.793Z
paper_authors._id: 637f228152229c63921119c3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/637f228152229c63921119c3/acwXorra1r9_7i3KlBFjS.jpeg
paper_authors.fullname: Zhiqiang Hu
paper_authors.type: user
paper_authors.user: Zhiqiang007
paper_authors._id: 6459d787130a95cb18fe52c0
paper_authors.name: Yunshi Lan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-24T10:57:00.694Z
paper_authors._id: 640447573d49e1e066b3ee12
paper_authors.avatarUrl: /avatars/9715081d88d130b27d29b050f5c733c7.svg
paper_authors.fullname: Yunshi Lan
paper_authors.type: user
paper_authors.user: YunshiECNU
paper_authors._id: 6459d787130a95cb18fe52c1
paper_authors.name: Roy Ka-Wei Lee
paper_authors._id: 6459d787130a95cb18fe52c2
paper_authors.name: Ee-Peng Lim
paper_title: Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning
    by Large Language Models
paper_summary: Large language models (LLMs) have recently been shown to deliver impressive
  performance in various NLP tasks. To tackle multi-step reasoning tasks,
  few-shot chain-of-thought (CoT) prompting includes a few manually crafted
  step-by-step reasoning demonstrations which enable LLMs to explicitly generate
  reasoning steps and improve their reasoning task accuracy. To eliminate the
  manual effort, Zero-shot-CoT concatenates the target problem statement with
  "Let's think step by step" as an input prompt to LLMs. Despite the success of
  Zero-shot-CoT, it still suffers from three pitfalls: calculation errors,
  missing-step errors, and semantic misunderstanding errors. To address the
  missing-step errors, we propose Plan-and-Solve (PS) Prompting. It consists of
  two components: first, devising a plan to divide the entire task into smaller
  subtasks, and then carrying out the subtasks according to the plan. To address
  the calculation errors and improve the quality of generated reasoning steps, we
  extend PS prompting with more detailed instructions and derive PS+ prompting.
  We evaluate our proposed prompting strategy on ten datasets across three
  reasoning problems. The experimental results over GPT-3 show that our proposed
  zero-shot prompting consistently outperforms Zero-shot-CoT across all datasets
  by a large margin, is comparable to or exceeds Zero-shot-Program-of-Thought
  Prompting, and has comparable performance with 8-shot CoT prompting on the math
  reasoning problem. The code can be found at
  https://github.com/AGI-Edgerunners/Plan-and-Solve-Prompting.
paper_ai_summary: A new prompting strategy, Plan-and-Solve (PS) and its extension PS+, improves the reasoning capabilities of LLMs by reducing errors in multi-step reasoning tasks, outperforming existing zero-shot methods.
paper_ai_keywords: large language models (LLMs) few-shot chain-of-thought (CoT) prompting zero-shot-CoT Plan-and-Solve (PS) Prompting PS+ prompting multi-step reasoning tasks calculation errors missing-step errors semantic misunderstanding errors subtasks GPT-3 zero-shot prompting Zero-shot-Program-of-Thought Prompting 8-shot CoT prompting