Unverified50% confidenceFactExact time
Generation strategies such as temperature parameter, top-k sampling, and nucleus sampling are all means of adjusting and controlling the probability distribution.
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9/11/2026
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VerifiedWhen generating each token, LLMs randomly sample from a probability distribution according to a temperature parameter rather than selecting the deterministic output with the highest probability, thereby producing emergent properties.75% 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.74% similarUnverified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74% similarUnverified在LLM引用行为测试中固定随机性需要同时设置temperature和top-p参数并在支持时固定随机种子72% similarUnverifiedLLM本质上是在做下一个token的概率预测，即便温度参数设为0，面对复杂的多步骤指令时，模型仍会受到注意力机制的影响72% similar
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