Unverified50% confidenceFactExact time
LLM在不同温度参数、上下文长度、随机种子下可能对同一攻击payload给出截然不同的响应，因此LLM安全测试的通过标准本质上是统计性的
1
Sources
50%
Confidence
Long-term
Relevance
7/17/2026
First Seen
Sources
Related Claims
UnverifiedSummaries generated by LLMs are probabilistic; the same input may produce different results at different temperature settings, posing a reproducibility risk.81% 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.80% similarUnverifiedLarge model outputs are stochastic (controlled by the Temperature parameter), so a single comparison is not conclusive and requires multiple sampling and statistical comparisons.71% similarUnverifiedLLM application reproducibility faces unique challenges, including model API version updates, randomness from temperature parameters, and changes in external tool states, requiring a complete environment snapshot to be recorded to ensure reproducibility70% similarUnverified在网络安全领域使用LLM作为裁判是有缺陷的，因为LLM总是会声称自己成功完成了攻击，必须为每个能力层级设计确定性的评分裁判70% similar
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