Unverified50% confidenceFactExact time
2022年DeepMind发布的Chinchilla论文修正了原始Scaling Laws，指出应在给定计算预算下同比例扩展参数量和训练数据量
1
Sources
50%
Confidence
Long-term
Relevance
7/14/2026
First Seen
Sources
热爱LLM却厌恶炒作：工程师如何理性看待大语言模型
Related Claims
UnverifiedChinchilla论文指出参数量与数据量需要等比例扩展64% similarUnverifiedOpenAI、DeepMind等机构的研究表明模型性能随参数量、数据量和计算量的增加呈幂律提升（Scaling Law）62% similarUnverified2023年多项学术研究证明，即使经过严格安全对齐训练的模型，当恶意指令以自然语言文档形式出现在上下文窗口中时，其遵循率仍显著高于直接注入场景60% similarUnverifiedThe marginal returns of simply increasing model parameters and training data are diminishing, a phenomenon known as 'Scaling Law slowdown.'60% similarUnverifiedAs model parameters scale from hundreds of billions to trillions, the performance gains delivered by each additional order of magnitude of compute become increasingly marginal — a phenomenon the academic community calls the 'ceiling effect' of Scaling Laws.59% similar
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