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Chain-of-thought prompting improves reasoning in large language models.

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SUPPORTED
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8 sources for · 0 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

Is it true that Chain-of-thought prompting improves reasoning in large language models?

Yes. The Wei et al. chain-of-thought paper reports that CoT prompting improves performance on arithmetic, commonsense and symbolic reasoning tasks across three large language models. Several narrower studies in legal reasoning, program repair, depression detection and bias evaluation also report CoT improving task performance in their specific domains.

Verdict: SUPPORTED · 8 sources for, 0 against · Last checked

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Evidence for · 8
Chain-of-Thought Reasoning Without Prompting
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Chain-of-Thought Reasoning Without Prompting. (n.d.). openreview.net. https://openreview.net/forum?id=NoD9czmRH6

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More for · 7
[2201.11903] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
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[2201.11903] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. (n.d.). arxiv.org. https://arxiv.org/abs/2201.11903

Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting
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Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting. (n.d.). cir.nii.ac.jp. https://doi.org/10.48550/arxiv.2401.15585

Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models
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Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models. (n.d.). cir.nii.ac.jp. https://doi.org/10.1109/embc58623.2025.11253093

Pyramid of Thought: A Novel Approach for Enhancing Chain-of-Thought Reasoning in Large Language Models
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Pyramid of Thought: A Novel Approach for Enhancing Chain-of-Thought Reasoning in Large Language Models. (n.d.). cir.nii.ac.jp. https://doi.org/10.11517/pjsai.jsai2024.0_3xin2111

A comparative study of large language models with chain-of thought prompting for automated program repair
2025
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Nurtantyana, E. D. R. A. G. R. (2025). A comparative study of large language models with chain-of thought prompting for automated program repair. archive.org. https://archive.org/details/10.11591ijai.v14.i6.pp4579-4589

Negative Contrastive Chain-of-Thought Distillation for Transfer Reasoning Capability to Small Language Models
2026
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Han, J., Jo, H., Hong, J., & Lee, S. (2026). Negative Contrastive Chain-of-Thought Distillation for Transfer Reasoning Capability to Small Language Models. doi.org. https://doi.org/10.1109/access.2026.3713532

Structured Evaluation of Legal Reasoning in LLMs: Chain-of-Thought Prompting and Human Scoring for Retrieval Robustness
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Structured Evaluation of Legal Reasoning in LLMs: Chain-of-Thought Prompting and Human Scoring for Retrieval Robustness. (n.d.). cir.nii.ac.jp. https://doi.org/10.20736/0002002106

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