Unverified50% confidenceFactExact time
Greedy decoding selects the highest-probability token at each step and is most effective at exposing the model's tendency toward repetition; sampling combined with a temperature parameter re-normalizes logits by dividing by temperature to trade off diversity against coherence
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50%
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Long-term
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10/12/2026
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UnverifiedCommon sampling strategies include top-k sampling (retaining only the k tokens with the highest probability), top-p sampling (dynamically selecting the minimum set of tokens whose cumulative probability just exceeds a threshold p), and the temperature parameter (controlling the sharpness of the distribution by scaling the logits).76% similarVerifiedTemperature controls the flatness of the sampling distribution; a higher value tends toward selecting lower-probability words, while Top-p dynamically narrows the candidate vocabulary set through a cumulative probability threshold.73% similarUnverifiedTop-p（核采样）通过限定候选token的累积概率范围来控制输出多样性，与Temperature配合使用可实现更精细的输出调节73% similarVerifiedSetting the temperature parameter too high in decoding strategies leads to increased sampling randomness, which is associated with hallucination generation71% similar
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