Unverified90% confidenceFactTime unknown
训练数据在输入模型前会经过严格的去重、过滤和质量筛选，低质量内容、重复文本、有害内容会被剔除
1
Sources
90%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedTraining data filtering pipelines typically include language identification, deduplication (e.g., MinHash approximate deduplication), classifier-based quality scoring, and URL-blacklist-based harmful content filtering.78% similarUnverifiedTraining data filters are designed to exclude obvious spam and pornographic content, not to detect carefully disguised narrative manipulation.74% similarUnverifiedTraditional large model training relies heavily on manually annotated data and external corpus collection, which are typically the bottlenecks constraining iteration speed.72% similarUnverifiedThe cleanliness of a benchmark is time-limited—once test questions are publicly circulated, future model training will very likely absorb them, causing fairness to decay accordingly.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/18948API
curl https://kongchang.com/api/v1/knowledge/claims/18948MCP
get_claim(id=18948)