Unverified50% confidenceTradeoffExact time
即便温度参数设为0，不同的系统提示词、对话上下文和模型版本更新都会导致LLM输出差异，使品牌可见度测量面临可复现性挑战
1
Sources
50%
Confidence
Long-term
Relevance
8/24/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedAfter a model version update, its attention allocation mechanism, temperature sensitivity, and instruction parsing method may change, causing existing prompt strategies to become ineffective.76% 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74% 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业界应对大模型非确定性的测试策略包括将温度设为 0、使用 LLM-as-Judge，以及使用 Mock 数据隔离 API 调用69% similarVerifiedThe 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.69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/794486API
curl https://kongchang.com/api/v1/knowledge/claims/794486MCP
get_claim(id=794486)