Models
A decoding strategy that samples several chain-of-thought reasoning paths for the same question and returns the final answer that appears most often.
A decoding strategy that samples several chain-of-thought reasoning paths for the same question and returns the final answer that appears most often. It replaces a single greedy reasoning path with a majority vote, on the intuition that different valid lines of reasoning tend to converge on the correct answer. The original Google paper reported gains such as +17.9% on GSM8K over standard chain-of-thought prompting. Unlike best-of-N, it needs no separate verifier, only answers that can be compared.
Primary source: arxiv.org
Example
The original Google paper reported gains such as +17.9% on GSM8K over standard chain-of-thought prompting.
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FAQ
A decoding strategy that samples several chain-of-thought reasoning paths for the same question and returns the final answer that appears most often.
Self-Consistency sits in the Models part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Developers Digest publishes tutorials and videos that cover Models topics including Self-Consistency. Check the blog and YouTube channel for hands-on walkthroughs.
Related
A prompting framework that has a language model explore several intermediate reasoning steps ("thoughts") as branches of a tree instead of one linear chain.
A single model that can either answer directly or think at length before answering, instead of shipping separate chat and reasoning models.
A dataset of 8,500 grade school math word problems from OpenAI that take several steps of basic arithmetic to solve.

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