Unverified50% confidenceFactExact time
When faced with questions outside the training distribution (out-of-distribution queries), models may degrade into guessing based on surface-level patterns.
1
Sources
50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThere is considerable evidentiary difficulty in technically determining whether one model has been distilled from another, as similarity in model outputs may stem from shared training data or similar architectural choices.78% similarUnverifiedIf leading models are used to generate training data, they may systematically pass their own biases to the next generation, a risk known as 'model collapse'.78% similarUnverifiedWhen input constraints are reduced, machine learning models rely more heavily on their prior distribution—that is, the default understanding of a concept formed during training.77% similarUnverified预训练模型迁移复用的假设是模型在源域学到的特征具有跨域泛化能力，但高层语义特征可能存在显著的域偏移76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/905412API
curl https://kongchang.com/api/v1/knowledge/claims/905412MCP
get_claim(id=905412)