Unverified50% confidenceFactExact time
An LLM outputs a probability distribution when generating each word, and the higher the temperature the more random the sampling; even if two assistants recall the same information sources, different temperature settings can lead to different wording or even a different order of companies mentioned.
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50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
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Related Claims
UnverifiedThe output of LLMs is inherently stochastic, and identical inputs may produce entirely different results across different calls.76% similarUnverifiedSummaries generated by LLMs are probabilistic; the same input may produce different results at different temperature settings, posing a reproducibility risk.75% similarUnverifiedLLM judge results are affected by factors such as temperature, prompt wording, and model version updates, causing the same set of test cases to potentially produce different conclusions when run at different times.74% similarVerifiedSetting the temperature parameter too high in decoding strategies leads to increased sampling randomness, which is associated with hallucination generation74% 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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