Unverified85% confidenceFactTime unknown
PEG=1被认为是合理估值，PEG<1可能被低估，PEG>2则需要警惕高估风险
1
Sources
85%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedSome models carry the risk of over-optimization toward test sets, causing leaderboard scores to deviate from real-world performance (benchmark saturation).66% similarUnverified对MoE模型的量化需要格外谨慎，因稀疏激活使专家参数数值分布集中，低比特量化易造成路由偏差导致能力退化61% similarUnverifiedThe optimal regularization strength needs to be selected through cross-validation; if it is too large it leads to underfitting, and if it is too small the regularization effect is negligible.61% similarVerified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61% similarUnverified为几个百分点的榜单差距进行定向优化是本末倒置，评估模型时不应盲目迷信榜单排名60% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/19011API
curl https://kongchang.com/api/v1/knowledge/claims/19011MCP
get_claim(id=19011)