Verified80% confidenceFactExact time
The core finding of CoT is that when models are asked to think step by step, accuracy on tasks such as arithmetic and commonsense reasoning improves substantially.
6
Sources
80%
Confidence
Long-term
Relevance
7/15/2026
First Seen
Sources
Related Claims
UnverifiedCoT通过让模型在给出最终答案前逐步推理,可在数学推理等复杂任务上将准确率提升40%以上82% similarVerifiedChain-of-Thought (CoT) prompting significantly improves accuracy on math and logical reasoning tasks by guiding the model through step-by-step reasoning.80% similarUnverifiedChain-of-Thought (CoT) prompting improves the quality of complex problem solving by requiring the model to reason step by step.80% similarUnverifiedExperiments show that ReAct achieves significant improvements over pure CoT methods on multi-hop reasoning tasks, and every reasoning step is traceable.78% similarUnverified研究表明,即使使用相同的基础模型,引入迭代反思机制后在代码生成、数学推理等任务上的准确率可提升20%-40%75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/516354API
curl https://kongchang.com/api/v1/knowledge/claims/516354MCP
get_claim(id=516354)