Expired80% confidenceFactExact time
The large model temperature parameter adjusts the flatness of the output probability distribution by dividing logits by the temperature value before applying softmax normalization.
8
Sources
80%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
Why Should Ordinary People Learn Python in the AI Era? The Path from User to Master
bilibiliCode老猫6/26/2026
Related Claims
Unverified温度参数（Temperature）的数学本质是对Softmax函数输出的概率分布进行缩放，趋近于0时模型进行贪心解码80% similarUnverifiedTemperature scaling is a calibration method that attempts to calibrate logits into more reliable probability estimates.79% similarVerifiedThe Temperature parameter of large models typically ranges from 0 to 2; the lower the temperature, the more deterministic and conservative the output, while the higher the temperature, the more diverse and creative the output.79% similarVerifiedThe Temperature parameter controls the randomness of LLM output; for code generation scenarios, a lower temperature value (such as 0.1) is typically recommended to obtain more deterministic and conservative results.78% similar
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