Unverified60% confidenceFactExact time
Large models store parametric knowledge, with world knowledge compressed and encoded into billions of neural network weights rather than being stored in a structured knowledge base that can be precisely retrieved.
2
Sources
60%
Confidence
Long-term
Relevance
7/6/2026
First Seen
Sources
Fixing the Three Big Weaknesses of LLMs: Knowledge, Computation, and Tool Use
bilibili晴天AI实战7/4/2026
Related Claims
UnverifiedThe knowledge of general-purpose large models comes from massive public corpora, which is broad but not deep.78% similarUnverifiedThe knowledge of large language models is encoded in a distributed manner across the parameter weight matrices, and the larger the number of parameters, the more long-tail knowledge can be retained.77% similarUnverified2022年Meng等人的「知识神经元」研究表明，事实性知识主要存储在MLP层的键值对中，通过修改特定MLP层的参数可以精确编辑模型的知识73% similarUnverifiedLLM的知识以分布式方式编码于数十亿参数的权重矩阵中，而非以符号化的逻辑规则存储73% 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 neurons73% similar
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