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chain of thought prompting elicits reasoning in large language models

Published by Www1 Stjameswinery

5 min read · May 30, 2026

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Oct 31, 2022 · Chain of thought prompting enables models to generate intermediate reasoning steps to help solve multi-step arithmetic, commonsense, and symbolic reasoning tasks.

Abstract We explore how generating a chain of thought—a series of intermediate reasoning steps—significantly improves the ability of large language models to perform complex reasoning. In …

Feb 1, 2023 · We propose an automatic prompting method (Auto-CoT) to elicit chain-of-thought reasoning in large language models without needing manually-designed demonstrations.

Abstract We explore how generating a chain of thought—a series of intermediate reasoning steps—significantly improves the ability of large language models to perform complex reasoning. In …

Sep 22, 2023 · The increasing scale of large language models (LLMs) brings emergent abilities to various complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is …

Sep 25, 2024 · This paper investigates an under-explored challenge in large language models (LLMs): chain-of-thought prompting with noisy rationales, which include irrelevant or inaccurate reasoning …

Chain of thought prompting elicits reasoning in large language models. In Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022), 2022b.

Abstract Chain-of-thought (CoT) prompting has been shown to empirically improve the accuracy of large language models (LLMs) on various ques-tion answering tasks. While understanding why CoT …

Sep 21, 2023 · Large language models (LLMs) can achieve impressive performance on various reasoning tasks by incorporating chain-of-thought (CoT) prompting, where step-by-step reasoning is …

Abstract Chain-of-thought (CoT) prompting can guide language models to engage in complex multi-step reasoning. The quality of provided demon-strations significantly impacts the success of downstream …