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Self-Consistency Improves Chain of Thought Reasoning in Language Models

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Denny Zhou · Jason Wei · Quoc Le · Xuezhi Wang

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Published 2022-03-21 authorship evidence authorship evidence authorship evidence authorship evidence

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Self-Consistency Improves Chain of Thought Reasoning in Language Models International Conference on Learning Representations · published 2023 · ICLR 2023. Located by the cross-family machine check on 2026-10-30 (item A048; PDF title page: “Published as a conference paper at ICLR 2023”; the official conference paper list), at the precision of its source; not read again at admission.
Self-Consistency Improves Chain of Thought Reasoning in Language Models arXiv · published 2022-03-21 · arXiv abstract page, retrieved for the candidate pack on 2026-10-28 (SHA-256 ebdbc91093fd99c0b0c74218fdc3df4619d652e964254eec79bf75cf055ba2dc). Submission history as arXiv records it (UTC): v1 21 March 2022; v2 6 April 2022; v3 4 October 2022; v4 7 March 2023. The page serves the latest version; each version is a separate expression, and only the one a record names was read.
Self-Consistency Improves Chain of Thought Reasoning in Language Models arXiv · published 2022-03-21 · arXiv:2203.11171v1, submitted 21 March 2022 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 ed1ef6f3ff521a53b9cb873e8fad7991984a5c747ca0ae92d83d4e01e4f39326). The author list is the byline as printed on the first page.

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In its own wordsThe paper presents self-consistency, an ensemble strategy that the authors report improves the reasoning accuracy of large language models.“We explore a simple ensemble strategy, self-consistency, that significantly improves the reasoning accuracy of large language models.” arXiv v1, PDF p. 1, abstract2022-03-21 · publication · Stated in source · Supported Inspect claim & evidence ↗