Unverified50% confidenceFactExact time
The temperature parameter controls sampling randomness: the lower the value, the more deterministic the output; near 0, the model tends to choose the highest-probability token, suitable for code generation, SQL generation, and other scenarios requiring reproducibility.
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10/14/2026
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VerifiedTemperature 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.78% similarUnverifiedGreedy decoding at temperature zero further suppresses the probability of a model outputting ESCALATE, because models tend to select the answer pattern that appeared most frequently during training, and escalation is inherently rare in instruction fine-tuning data.75% similarVerifiedLarge model outputs are stochastic (controlled by the Temperature parameter), so a single comparison is not conclusive and requires multiple sampling and statistical comparisons.75% similarUnverifiedGreedy 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 coherence74% similarVerifiedThe non-determinism of LLMs stems from sampling strategies; the Temperature parameter controls the randomness of the output distribution, while Top-p and Top-k further constrain the range of candidate tokens.73% similar
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