Unverified60% confidenceFactExact time
Since the Transformer architecture was proposed by Google in 2017, its self-attention mechanism has a computational complexity that grows quadratically with sequence length
2
Sources
60%
Confidence
Long-term
Relevance
7/7/2026
First Seen
Sources
LangChain+MCP实战：大模型选型与本地部署避坑指南
bilibili码士集团-马小琪7/5/2026
Related Claims
UnverifiedThe Transformer was released by Google in 2017, and its self-attention mechanism allows the model to dynamically compute the relevance weights between a token and all other tokens in the sequence.81% similarUnverifiedConformer（Convolution-augmented Transformer）架构由Google在2020年提出，结合了Transformer的全局自注意力机制和卷积神经网络的局部特征提取能力78% similarVerifiedThe Transformer architecture was proposed by Google in 2017.77% similarUnverified2017年谷歌发表《Outrageously Large Neural Networks》，系统验证了稀疏MoE在Transformer结构中的有效性77% similarUnverifiedGoogle Brain于2017年在《Outrageously Large Neural Networks》论文中将MoE规模化应用于Transformer框架77% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/181375API
curl https://kongchang.com/api/v1/knowledge/claims/181375MCP
get_claim(id=181375)