Unverified75% confidenceOpinionTime unknown
大语言模型的参数量从数十亿到数千亿不等,本质上是对训练数据中统计规律的压缩存储
1
Sources
75%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedLarge language models do not store raw text in the traditional sense during training; instead, they encode statistical patterns into billions of parameters through the backpropagation algorithm.84% similarUnverified大语言模型通过自监督预训练学习token序列的条件概率分布,本质上是统计压缩模型,无法通过统计特征区分正确与错误的数学证明77% similarVerifiedWhen input information is incomplete, large language models complete it based on the most common patterns in their training data, which is statistically called maximum likelihood estimation.76% similarUnverifiedThe facts absorbed by large language models during the pre-training phase are encoded in a distributed manner across billions of parameters, with a single fact diffused across the activation patterns of hundreds or even thousands of neurons76% similarUnverifiedModern large language models form complex weight networks of billions of parameters through gradient descent optimization on massive datasets, with no human engineer individually designing each 'component'75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/21128API
curl https://kongchang.com/api/v1/knowledge/claims/21128MCP
get_claim(id=21128)