Chain-of-Thought Prompting Elicits Reasoning
Chain-of-Thought Prompting Elicits Reasoning is one of 15 landmark papers in TheLLMWiki's research index — explained here in plain language, without the jargon.
What this paper showed
Showed that prompting a large language model to write out intermediate reasoning steps before giving its final answer substantially improves performance on arithmetic, logic and multi-step reasoning tasks — with the effect only appearing reliably once models were large enough.
For the full technical detail — architecture diagrams, training data, ablations and exact benchmark numbers — the original paper is the authoritative source; this page exists to give you the plain-language version before you decide whether to read further.
The lasting impact of Chain-of-Thought Prompting Elicits Reasoning
Chain-of-thought prompting is now built directly into reasoning models like o3 and DeepSeek-R1, rather than something a user has to ask for explicitly — this paper is the reason "let's think step by step" became a standard technique in the first place.
Papers earn a place in this index specifically because their core idea is still visible in production systems today, not just because they were influential when published. If you're trying to understand why a current model or technique works the way it does, tracing it back to a paper like this one is usually more useful than reading a summary of the model's release notes alone.
Chain-of-Thought Prompting Elicits Reasoning, answered
Who published Chain-of-Thought Prompting Elicits Reasoning?
This paper came out of Google.
Why does this paper matter today?
Chain-of-thought prompting is now built directly into reasoning models like o3 and DeepSeek-R1, rather than something a user has to ask for explicitly — this paper is the reason "let's think step by step" became a standard technique in the first place.
Where can I read the full paper?
Search the paper title on arXiv or Google Scholar for the original PDF and any follow-up work that has cited it.
Do I need to read the full paper to understand the idea?
Not necessarily — the plain-language summary above covers the core contribution. The full paper matters most if you're implementing the technique yourself or need the exact experimental details.
What should I read next?
See the related papers above, or the AI research hub for more landmark work organized by topic.
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