Unverified50% confidenceFactExact time
传统控制模型多样性的方法是通过调整温度、Top-p等采样参数
1
Sources
50%
Confidence
Long-term
Relevance
7/14/2026
First Seen
Sources
口语化采样登顶ICML：提示工程配得上顶会吗？
Related Claims
VerifiedTraditional classification models can improve calibration through post-processing methods such as Temperature Scaling.80% similarUnverifiedModel capability evaluation must fix inference parameters (temperature, top-p, etc.); otherwise, the same model may exhibit vastly different performance under different parameter configurations.77% similarUnverifiedThe output of large language models is inherently probabilistic, and parameters such as sampling temperature and Top-P mean the model may generate different content each time, posing a challenge for software testing.72% similarVerifiedWhen a large language model generates each token, it samples from a probability distribution; the temperature parameter controls the degree of randomness—higher temperature produces more diverse outputs, while lower temperature produces more deterministic ones.71% similar
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