Zero-shot step-by-step prompting

A simple step-by-step instruction improved reasoning results without worked examples in the prompt.

Takeshi Kojima and colleagues

AI topics

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The contribution

The paper investigated prompting language models to reason through a problem before extracting an answer. The phrase “Let's think step by step” improved performance on several benchmark tasks without supplying task-specific reasoning demonstrations.

What this does not establish

The effect is empirical and task-dependent. It neither guarantees valid reasoning nor makes every model a reliable zero-shot reasoner.

Why this date?

The preprint was submitted on 24 May 2022. This is the source for the familiar zero-shot phrase, distinct from Wei and colleagues' worked-example method.

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The original work

Large Language Models are Zero-Shot Reasoners

Authors: Takeshi Kojima and colleagues
Publication: arXiv:2205.11916; NeurIPS 2022
Source type: Preprint / conference paper

Paper and version history

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Cited By

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Constitutional AI: Harmlessness from AI Feedback (2022)
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GPT-4 Technical Report (2023)
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Citations

Works in the Atlas cited by this entry.

Attention Is All You Need (2017)
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018)
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Language Models are Unsupervised Multitask Learners (2019)
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PyTorch: An Imperative Style, High-Performance Deep Learning Library (2019)
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Language Models are Few-Shot Learners (2020)
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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (2022)
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Training language models to follow instructions with human feedback (2022)
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The contribution

What this does not establish

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