Pith Number

pith:FQMCDKHN

pith:2023:FQMCDKHNVWX7RW6KQIJFTMP55X

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Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

Ee-Peng Lim, Lei Wang, Roy Ka-Wei Lee, Wanyu Xu, Yihuai Lan, Yunshi Lan, Zhiqiang Hu

Plan-and-solve prompting divides tasks into subtasks before solving them to cut missing-step errors in zero-shot chain-of-thought reasoning.

arxiv:2305.04091 v3 · 2023-05-06 · cs.CL

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\pithnumber{FQMCDKHNVWX7RW6KQIJFTMP55X}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

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.

C2weakest assumption

That the observed gains arise specifically from the plan-then-solve structure rather than from increased prompt length, additional instructions, or other uncontrolled prompt-engineering factors.

C3one line summary

Plan-and-Solve prompting improves zero-shot LLM reasoning by first creating an explicit plan then executing subtasks, outperforming simple 'think step by step' prompts across ten datasets.

References

25 extracted · 25 resolved · 3 Pith anchors

[1] On the advance of making language models better reasoners.arXiv preprint arXiv:2206.02336, 2 2016

[2] LLM+P: Empowering Large Language Models with Optimal Planning Proficiency 2019 · arXiv:2304.11477

[3] Measuring and Narrowing the Compositionality Gap in Language Models 2080 · arXiv:2210.03350

[4] LaMDA: Language Models for Dialog Applications 2023 · arXiv:2201.08239

[5] Convert A cents to dollars

Formal links

2 machine-checked theorem links

Cited by

28 papers in Pith

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Runtime-Structured Task Decomposition for Agentic Coding Systems

Commander-GPT: Dividing and Routing for Multimodal Sarcasm Detection

The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences

Receipt and verification

First computed	2026-05-17T23:38:48.467034Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2c1821a8edadaff8dbca821259b1fdedc7d205881b6610029e1f9bfde20428ce

Aliases

arxiv: 2305.04091 · arxiv_version: 2305.04091v3 · doi: 10.48550/arxiv.2305.04091 · pith_short_12: FQMCDKHNVWX7 · pith_short_16: FQMCDKHNVWX7RW6K · pith_short_8: FQMCDKHN

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/FQMCDKHNVWX7RW6KQIJFTMP55X \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 2c1821a8edadaff8dbca821259b1fdedc7d205881b6610029e1f9bfde20428ce

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "f065ef4bf5e68e80903b1ad1da282d69025925c2b3235bae6ac3930ef1d10a4a",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2023-05-06T16:34:37Z",
    "title_canon_sha256": "f56516d1ca947cb363110d8bea974c14791c50e9325777e0a715a28c362e970d"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2305.04091",
    "kind": "arxiv",
    "version": 3
  }
}