Unverified50% confidenceFactExact time
When holdout samples are used for score estimation, the ordering scores are inflated in expectation, and this inflation occurs for candidate nodes that should not yet be selected, so the direction of the bias is favorable for correct ordering.
1
Sources
50%
Confidence
Long-term
Relevance
10/5/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedEvaluation sets suffer from representational bias, as they can only cover situations the builders anticipated, meaning a model that performs perfectly on the eval set may still fail on real traffic after launch.71% similarUnverifiedDuring fine-tuning, hard negatives (e.g., positions in the same field but at a different seniority level) should be used instead of random negatives to obtain maximum gains.71% similarUnverified多次采样取多数票或自洽性方法可在推理层降低单次随机性对最终结果的影响71% similarUnverified限额校验往往发生在请求入队时,而实际token消耗统计发生在推理完成后,两者之间存在时间差71% similarUnverifiedIn machine learning evaluation, precision and recall often have a trade-off relationship, and increasing recall typically leads to more genuine products being misclassified.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/976016API
curl https://kongchang.com/api/v1/knowledge/claims/976016MCP
get_claim(id=976016)