Verified70% confidenceFactExact time
Large 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.
4
Sources
70%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
纽约时报指控OpenAI隐匿证据，ChatGPT版权诉讼再升级
rss7/9/2026
Related Claims
Unverified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84% similarUnverified大型语言模型的训练数据管道通常包含数据采集、去重、过滤、分词等阶段，每个阶段均会产生可追溯的元数据和处理日志82% 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.82% 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 neurons82% similarUnverifiedLanguage models are fundamentally probabilistic text generation systems that produce content based on statistical patterns in training data rather than understanding facts80% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/494399API
curl https://kongchang.com/api/v1/knowledge/claims/494399MCP
get_claim(id=494399)