Unverified50% confidenceFactExact time
When 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.
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9/21/2026
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UnverifiedStudent's t-distribution is a heavy-tailed distribution whose tails decay more slowly than the Gaussian distribution, making it more robust to outliers.76% similarUnverifiedA 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.75% similarUnverifiedAssuming the data follows a Gaussian distribution, maximizing the likelihood is equivalent to minimizing the mean squared error.68% similarUnverifiedIn robust regression, replacing the likelihood function from Gaussian to a t-distribution is equivalent to reducing the weight of outliers, because the log-likelihood of the t-distribution penalizes extreme values more slowly.67% similarUnverified配对 t 检验假设两组差值服从正态分布，适用于样本量较大（通常 n>30）的场景；Wilcoxon 符号秩检验是其非参数替代方案，仅要求差值分布对称63% similar
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