Unverified50% confidenceFactExact time
Chain-of-thought technology shows noticeable effects on models with more than 100B parameters.
1
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50%
Confidence
Long-term
Relevance
9/9/2026
First Seen
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Related Claims
UnverifiedChain-of-thought techniques only produce significant effects on large models with more than 100 billion parameters.77% similarUnverifiedThe Chain-of-Thought technique can significantly improve model accuracy on mathematical reasoning and code generation tasks, with the most pronounced effect on large models with parameter scales exceeding 100B.74% similarUnverifiedResearchers found that when model parameters exceed a threshold of approximately 10 billion, models exhibit a nonlinear emergent capability leap in tasks such as logical reasoning and multi-step planning.72% similarUnverifiedCombining knowledge distillation with quantization can enable a billion-parameter-scale model to retain 70–85% of the original large model's capabilities while achieving a 5–10× improvement in inference speed.70% similarUnverified2021年的Chain-of-Thought Prompting论文证明，通过在提示词中加入推理步骤示例，模型的数学推理准确率可提升数十个百分点68% similar
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