Unverified60% confidenceFactExact time
Even with temperature set to 0 (greedy decoding), the behavior of large language models is still influenced by factors such as context length and positional bias in the attention mechanism.
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60%
Confidence
Long-term
Relevance
8/23/2026
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VerifiedThe non-determinism of large language models stems from their autoregressive sampling process; even with temperature set to 0, differences in inference frameworks, batch sizes, and hardware floating-point operations can all cause output drift.78% similarUnverifiedEven when temperature is set to 0, different conversation histories and system prompts may cause minute shifts in the attention weights of the Transformer self-attention mechanism, thereby producing different numerical outputs.77% similarUnverified即便temperature设为0，LLM在长对话上下文中仍可能出现幻觉或遗漏必要披露，不宜依赖模型生成强监管行业的合规话术73% similarUnverifiedAfter a model version update, its attention allocation mechanism, temperature sensitivity, and instruction parsing method may change, causing existing prompt strategies to become ineffective.73% 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.72% similar
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