--- title: "Large language models are zero-shot reasoners — Kojima 2022" description: "The paper asks whether large language models need hand-written, task-specific worked examples to solve multi-step reasoning problems." authors: ["Takeshi Kojima", "Shixiang Shane Gu", "Machel Reid", "Yutaka Matsuo", "Yusuke Iwasawa"] year: 2022 venue: "neural information processing systems" source: "https://arxiv.org/abs/2205.11916" arxiv: "2205.11916" licence: "arXiv — non-exclusive licence to distribute" licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2022](https://md.research.elamur.ai/free/en/papers/2022/page/3/) / Large language models are zero-shot reasoners # Large language models are zero-shot reasoners **Authors:** Takeshi Kojima, Shixiang Shane Gu, [Machel Reid](https://md.research.elamur.ai/free/en/authors/machel-reid/), [Yutaka Matsuo](https://md.research.elamur.ai/free/en/authors/yutaka-matsuo/), Yusuke Iwasawa · **Year:** 2022 · **Venue:** neural information processing systems · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/) **Links:** [Publisher's page](https://doi.org/10.52202/068431-1613) · [PDF](https://arxiv.org/pdf/2205.11916) · [arXiv 2205.11916](https://arxiv.org/abs/2205.11916) · [OpenAlex](https://openalex.org/W7133231369) **Citations:** 1557 citations (openalex-snapshot · 2026-10-14) · [2223](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary The paper asks whether large language models need hand-written, task-specific worked examples to solve multi-step reasoning problems. Earlier chain of thought (CoT) work relied on few-shot demonstrations. The authors propose Zero-shot-CoT, which uses no examples at all. The model receives the question followed by one fixed trigger, "Let's think step by step," and writes out its reasoning. A second prompt then pulls a correctly formatted answer out of that generated text. The same template was applied unchanged across arithmetic benchmarks (MultiArith, GSM8K, AQUA-RAT, SVAMP), symbolic tasks (Last Letter, Coin Flip), commonsense sets (CommonSenseQA, StrategyQA), and BIG-bench logic tasks (Date Understanding, Tracking Shuffled Objects). The authors tested InstructGPT, original GPT-3 and PaLM. Zero-shot-CoT far outperforms plain zero-shot prompting, although it still trails carefully engineered Few-shot-CoT. Its gains also increase with model size. The work matters because it sets a minimal, strong zero-shot baseline for reasoning tasks. It also suggests that LLMs hold general reasoning abilities that a single generic prompt can bring out. ## Key points - With InstructGPT (text-davinci-002), compared with standard zero-shot prompting, MultiArith accuracy climbs to 78.7% (up from 17.7%) and GSM8K accuracy climbs to 40.7% (up from 10.4%). - PaLM (540B) shows improvements of a similar size on MultiArith and GSM8K. Gains also appear for vanilla GPT-3, so the method works independently of instruction tuning. - The pipeline has two stages: a reasoning-extraction prompt, then an answer-extraction prompt (e.g., asking for the answer in arabic numerals). - Few-shot-CoT stays stronger, but its accuracy falls when the example question types do not match the target task. Zero-shot-CoT needs no per-task engineering. - The authors name two limitations: little public information about the LLMs' training data, and biases the models inherit from web corpora. They argue the consistent gains across models point to general reasoning rather than memorization. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Mathematics & statistics](https://md.research.elamur.ai/free/en/shelves/mathematics/) ## Cited by Cited by 2223 papers of our library cite it (of the 140673 whose bibliographies we read); 152 of them have a page here, the first 100 below, 2071 not yet. - [Towards Safer Hebrew Communication: A Dataset for Offensive Language Detoxification](https://md.research.elamur.ai/free/en/papers/aclbulk-2025-vanetik-safer-hebrew-communication-dataset-offensive/) - [Quiet-STaR: Language models can teach themselves to think before speaking](https://md.research.elamur.ai/free/en/papers/al-2024-quiet-star-language-models-teach/) - [ArabicNumBench: Evaluating Arabic Number Reading in Large Language Models](https://md.research.elamur.ai/free/en/papers/alhumud-2026-arabicnumbench-evaluating-arabic-number-reading/) - [Sacred or Synthetic? 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BibTeX from the library's record of the work. ```bibtex @inproceedings{kojima2022large, title = {{Large language models are zero-shot reasoners}}, author = {Kojima, Takeshi and Gu, Shixiang Shane and Reid, Machel and Matsuo, Yutaka and Iwasawa, Yusuke}, booktitle = {neural information processing systems}, year = {2022}, doi = {10.52202/068431-1613}, eprint = {2205.11916}, archivePrefix = {arXiv}, url = {https://doi.org/10.52202/068431-1613} } ``` - [← previous Large Language Models Are Human-Level Prompt Engineers](https://md.research.elamur.ai/free/en/papers/zhou-2022-large-language-models-human-level/) - [next → Large Language Models Encode Clinical Knowledge](https://md.research.elamur.ai/free/en/papers/singhal-2022-large-language-models-encode-clinical/) - [↑ back to papers of 2022](https://md.research.elamur.ai/free/en/papers/2022/page/3/) ## References 49 references, 25 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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