Unverified50% confidenceFactExact time
The output of large models is probabilistic and non-deterministic, making it difficult to determine whether a new version is better during canary releases, requiring statistical evaluation over a large number of samples.
1
Sources
50%
Confidence
Long-term
Relevance
8/31/2026
First Seen
Sources
Related Entities
Related Claims
Verified若用含测试集的全量数据计算均值和标准差,会导致预处理泄漏,使评估指标虚高,无法真实反映模型泛化能力75% similarUnverifiedLarge model outputs are stochastic (controlled by the Temperature parameter), so a single comparison is not conclusive and requires multiple sampling and statistical comparisons.73% similarUnverified若将验证集或测试集数据纳入 fit,会造成数据泄露,导致模型评估结果过于乐观72% similarVerifiedSome models carry the risk of over-optimization toward test sets, causing leaderboard scores to deviate from real-world performance (benchmark saturation).72% similarUnverifiedThis research conclusion refutes the popular claim that models adaptively adjust their computation based on problem difficulty.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/829301API
curl https://kongchang.com/api/v1/knowledge/claims/829301MCP
get_claim(id=829301)