Unverified50% confidenceTradeoffExact time
The low recall indicates that relying solely on the top two principal axes with the greatest variance cannot cover all style dimensions that humans care about; extending to more principal components can improve recall but introduces noisy axes that cause precision to decline.
1
Sources
50%
Confidence
Long-term
Relevance
9/18/2026
First Seen
Sources
Related Entities
Related Claims
Unverified块太小会导致缺乏上下文,块太大会引入噪声并受限于模型上下文窗口长度,切块参数存在权衡74% similarUnverifiedAn embedding variance threshold that is too low may misclassify normal focused thinking as a loop.70% similarUnverifiedA similarity threshold that is too high may cause valid answers to be incorrectly filtered out, while one that is too low may introduce noise, requiring a trade-off between recall and precision.70% similarUnverifiedHigh bias means the model is too simple and leads to underfitting, while high variance means the model has learned noise and leads to overfitting, and the two tend to trade off against each other.70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/931139API
curl https://kongchang.com/api/v1/knowledge/claims/931139MCP
get_claim(id=931139)