Unverified50% confidenceFactExact time
Chinchilla论文指出参数量与数据量需要等比例扩展
1
Sources
50%
Confidence
Long-term
Relevance
8/14/2026
First Seen
Sources
Related Claims
Unverified2022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64% similarUnverified推理部署对单次计算规模要求低,但需要应对海量并发请求,更强调吞吐量和延迟的平衡60% 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)58% similarUnverifiedWhen the model needs to request complete data via a hash value, an additional round trip is incurred, which in certain cases may consume more tokens than not using Headroom.57% similarUnverified涌现能力指大模型某些能力在特定参数量阈值后突然出现,由Wei et al. 2022年论文系统记录57% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/746418API
curl https://kongchang.com/api/v1/knowledge/claims/746418MCP
get_claim(id=746418)