Unverified50% confidenceFactExact time
Transformer模型通过注意力机制(Self-Attention)在推理阶段在词汇表上生成一个概率分布,再通过温度参数(Temperature)调节分布的平坦度,温度越高输出越随机多样,温度越低越趋向高概率词元
1
Sources
50%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
AI智能体安全攻防实战:7大攻击手法与五层防御体系
bilibiliAI课堂7/9/2026
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80% similarUnverified大语言模型输出的非确定性源于 Transformer 解码阶段的随机采样机制,采样温度、Top-P 等参数决定每次生成内容可能不同76% similarUnverified大语言模型基于Transformer架构,其输出本质上是概率采样过程,由Temperature参数控制采样随机性74% similarUnverifiedLarge language models use the self-attention mechanism in the Transformer architecture to compute the probability distribution of all candidate tokens at each position74% similarUnverifiedLLM本质上是在做下一个token的概率预测,即便温度参数设为0,面对复杂的多步骤指令时,模型仍会受到注意力机制的影响73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/501745API
curl https://kongchang.com/api/v1/knowledge/claims/501745MCP
get_claim(id=501745)