Unverified80% confidenceOpinionExact time
LLMs do not understand knowledge the way humans do, but capture complex probability distributions in text through billions or trillions of parameters
1
Sources
80%
Confidence
Long-term
Relevance
8/2/2026
First Seen
Sources
From Medieval Grimoires to AI: Humanity's Thousand-Year Quest for Instant Knowledge
Related Entities
Related Claims
UnverifiedLLM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% similarVerifiedLLMs are fundamentally probability distribution systems trained on massive amounts of text, where each output is a sampling of the next most likely token rather than retrieval and reasoning based on real-world knowledge.73% similarUnverifiedWhen LLMs are scaled to tens of billions or even trillions of parameters, emergent abilities such as reasoning, translation, and code generation arise.70% similarVerifiedThe generation mechanism of LLMs is probability-based text prediction, rather than truly 'understanding' code semantics69% similarVerifiedThe core principle of large models is probabilistic text continuation, and the output is probabilistic sampling rather than deterministic output.65% similar
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