Verified90% confidenceFactExact time
The scaling laws established by Kaplan et al. in 2020 indicate that model performance follows a power-law relationship with parameter count, data volume, and compute.
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7/15/2026
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Related Claims
Unverified扩展假设由OpenAI研究员Jared Kaplan等人于2020年的扩展定律论文中量化阐述，核心是模型性能与参数量、数据量、计算量存在幂律关系84% similarVerifiedThe Scaling Law was first systematically quantified by Kaplan et al. in 2020 in language model research, pointing out a power-law relationship between model performance and parameter count, data volume, and compute.82% similarUnverified2020年Kaplan等人提出原始Scaling Law，倾向于在固定算力下优先扩大模型参数量82% similarVerifiedIn 2020, Kaplan et al. at OpenAI revealed that model performance follows predictable power-law relationships with parameter count, training data volume, and compute budget.79% similarUnverifiedThe neural network scaling laws proposed by Kaplan et al. in 2020 indicate that model performance has a power-law relationship with data volume, parameter count, and compute, where the model's cross-entropy loss L and parameter count N satisfy L ∝ N^(-α).78% similar
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