Unverified70% confidenceFactExact time
A higher temperature value makes the model's output probability distribution smoother and increases randomness; Top-p sampling draws only from the smallest set of tokens whose cumulative probability reaches the threshold.
2
Sources
70%
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
UnverifiedWhen T=1 the output is a standard softmax distribution; when T>1 the probability distribution becomes more uniform—the higher the temperature, the flatter the output distribution, and the more fine-grained information the student model captures.75% similarUnverifiedDifferent hyperparameters such as system prompt, temperature, maximum token count, and number of retries can cause the evaluation results of the same model to differ by more than 10 percentage points.74% similarUnverifiedThe two levels of stochasticity in doubly stochastic optimization are mini-batch random sampling over the data and Monte Carlo estimation of the variational expectation.71% similarUnverifiedTop-p (nucleus sampling) filters out extremely low-probability outputs by truncating the long tail of the probability distribution.69% similarUnverifiedAssuming the data follows a Gaussian distribution, maximizing the likelihood is equivalent to minimizing the mean squared error.68% similar
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