Unverified50% confidenceFactExact time
Scaling laws were initially widely validated in language model research, referring to the predictable power-law relationship of model performance as model scale, data volume, and compute increase.
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10/28/2026
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VerifiedThe 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.79% similarVerifiedScaling Law research has found that model performance exhibits a smooth power-law relationship with the number of model parameters N, dataset size D, and training compute C75% similarUnverifiedScaling Law research found that model performance (measured by cross-entropy loss) has a predictable power-law relationship with model parameter count, dataset size, and computational cost.74% similarVerifiedThere is a positive correlation between a model's calibration capability and model scale; larger models can more finely distinguish between states of knowledge.73% similarVerifiedCurrent large language models and generative models are fundamentally seeking statistical patterns in massive amounts of data71% similar
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