Confidently wrong answers are penalized. So are unnecessarily uncertain correct ones.
RLCR方法通过惩罚过度自信的错误答案和不必要的确定性正确的答案，来鼓励模型表达不确定性。
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RLCR reduced calibration error by up to 90 percent while maintaining or improving accuracy
这一关键实验结果表明，RLCR方法在减少校准误差的同时，保持了甚至提高了模型的准确性，表明其有效性。
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