Verified80% confidenceFactExact time
When 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.
32
Sources
80%
Confidence
Long-term
Relevance
12/21/2024
First Seen
Sources
Building a SaaS Website with AI and Zero Code: A Complete Bolt + Cursor Walkthrough
bilibiliAI进修生12/21/2024
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VerifiedThe randomness of AI large language model outputs stems from the temperature parameter and sampling strategy; when generating each token, the model is essentially performing probabilistic sampling.84% similarUnverifiedThe output of large language models is inherently probabilistic, and parameters such as sampling temperature and Top-P mean the model may generate different content each time, posing a challenge for software testing.83% similarUnverifiedLarge language models use probability-based sampling strategies during text generation, where hyperparameters like temperature and top-p determine the degree of randomness in output.80% similarUnverified大语言模型（LLM）由于temperature、top-p、top-k等采样策略带有随机性，同一提示词在两次调用中可能产生截然不同的响应80% similarVerifiedWhen 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.78% similar
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