Unverified50% confidenceFactExact time
LLM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
1
Sources
50%
Confidence
Long-term
Relevance
7/20/2026
First Seen
Sources
Related Claims
UnverifiedLLMs do not understand knowledge the way humans do, but capture complex probability distributions in text through billions or trillions of parameters73% similarUnverifiedLarge 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.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.72% similarUnverifiedThe essence of LLMs is statistical probability-based sequence prediction rather than true logical reasoning, which creates inherent limitations when handling long-context dependencies, global architectural consistency, and complex edge cases.71% similarUnverified模型微调能将知识永久写入模型权重，但需要大量标注数据、算力成本高、迭代周期长69% similar
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