Verified65% confidenceFactExact time
LLM生成每个token时根据温度参数从概率分布中随机采样，而非选择概率最高的确定性输出，从而产生涌现性
3
Sources
65%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
Related Claims
UnverifiedLLM基于Transformer架构的自注意力机制和Softmax概率分布，通过温度参数控制采样随机性，温度趋近0时输出最高概率token，温度升高时引入更多随机性82% similarPartially VerifiedLLM采用自回归生成机制，每一步仅根据概率分布采样下一个token81% similarUnverifiedLLM output is inherently stochastic, controlled by sampling parameters like temperature.81% similarUnverifiedLLM可通过分析输出token的对数概率分布、思维链末尾自我评估、温度采样统计答案分布熵值等手段近似估计决策置信度79% similarUnverified在LLM引用行为测试中固定随机性需要同时设置temperature和top-p参数并在支持时固定随机种子79% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/502737API
curl https://kongchang.com/api/v1/knowledge/claims/502737MCP
get_claim(id=502737)