Verified75% confidenceFactExact time
Alignment training optimizes the model's statistical behavior in known scenarios rather than establishing formally verifiable security boundaries, and may fail when faced with rare instruction combinations or role-playing frameworks outside the training distribution.
5
Sources
75%
Confidence
Long-term
Relevance
7/20/2026
First Seen
Sources
Related Claims
UnverifiedSimply stacking parameters without sufficient training data results in a model that is large but not capable; a leap in parameters must be accompanied by a proportionally expanded training corpus and computing power to realize capability gains.81% similarUnverified实验表明按由易到难顺序喂给模型训练样本能加速收敛并学到更鲁棒的特征表示，性能优于随机打乱顺序的训练79% similarUnverifiedThe training set is used to fit model parameters, the validation set is used to tune hyperparameters and prevent overfitting, and the test set is used for an unbiased estimate of the model's generalization ability; the three must be strictly independent.79% similarUnverifiedTraditional large model training relies heavily on manually annotated data and external corpus collection, which are typically the bottlenecks constraining iteration speed.79% similarVerifiedTraining data poisoning can implant persistent backdoors at the model weight level, posing far greater risks than application-layer jailbreaks, but the implementation threshold is higher and typically requires control over the training pipeline.79% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/566648API
curl https://kongchang.com/api/v1/knowledge/claims/566648MCP
get_claim(id=566648)