Self-Consistency Improves Chain of Thought Reasoning in Language Models

Chain-of-thought prompting combined with pre-trained large language models has achieved encouraging results on complex reasoning tasks. Self-consistency leverages the intuition that a complex reasoning problem typically admits multiple different ways of thinking leading to its unique correct answer.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 In this paper, we propose a new decoding strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought prompting.
• 2 It first samples a diverse set of reasoning paths instead of only taking the greedy one, and then selects the most consistent answer by marginalizing out the sampled reasoning paths.
• 3 Self-consistency leverages the intuition that a complex reasoning problem typically admits multiple different ways of thinking leading to its unique correct answer.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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