Unverified50% confidenceFactExact time
从信息论和香农定理角度看，任何超过信息熵下界的压缩都必然丢失信息，因此无论模型多大，压缩必然是有损的
1
Sources
50%
Confidence
Long-term
Relevance
7/15/2026
First Seen
Sources
Related Claims
Unverified信息论先驱香农指出任何信道容量都存在上限，当信源信息熵超过信道容量时信息损失不可避免78% similarUnverifiedReasoning capabilities can be compressed through knowledge distillation and reinforcement learning fine-tuning, but the storage of factual knowledge depends on parameter scale, and compression leads to reduced knowledge coverage and higher hallucination rates.78% similarUnverifiedThe concept of information entropy proposed by Shannon indicates that low-probability events carry higher information content.74% similar
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