Unverified50% confidenceFactExact time
Using the median rather than the mean is to avoid disturbance of the metric by long-tail samples and to better reflect the handling experience of typical issues.
1
Sources
50%
Confidence
Long-term
Relevance
10/13/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedCatching erroneous joins and duplicate counting requires additional protection layers: row count conservation checks, join uniqueness validation, result sanity previews, and predefined metrics rather than ad hoc calculations.77% 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.75% similarUnverifiedTargeted alignment data—supplementing alignment samples specifically tailored to the domains where misalignment occurs—can significantly reduce context confusion.75% 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74% similarUnverifiedThe paper finds that whether using a full sampler or an early-stopped sampler, error bounds of the same order can be achieved (consistent up to logarithmic factors).74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1020716API
curl https://kongchang.com/api/v1/knowledge/claims/1020716MCP
get_claim(id=1020716)