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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models research paper by Google, 2022

Google · Jan 28, 2022 · Reasoning · 22,107 citations · 16 upvotes

Read on arXiv

What it shows

Asking a model to write out its intermediate steps (chain of thought) sharply improves its math and logic answers.

Summarised by hand from the abstract.

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Other reasoning papers

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DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement LearningReinforcement learning taught a model long step-by-step reasoning on par with OpenAI o1.DeepSeek · Jan 20255,691
About this paper
Authors
Jason Wei, Xuezhi Wang, Dale Schuurmans and 6 more
arXiv
2201.11903 · PDF
Venue
Neural Information Processing Systems
Citations
22,107, 1,410 influential · Semantic Scholar
Upvotes
16 · Hugging Face
Lab
Google · on Companies · on Acquisitions · on Paydays · on TechConf · on Releases

Changes

What changed
New paperAdded to the listSep 24, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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