Unverified50% confidenceOpinionExact time
Existing large models are trained for human preferences and are prone to mode collapse, overconfidence, and lack of reliability in machine-use scenarios.
1
Sources
50%
Confidence
Long-term
Relevance
9/17/2026
First Seen
Sources
Related Entities
Related Claims
Unverified智能体的偏好受训练数据、提示词、版本迭代等多重因素影响,今天有效的优化手段可能在下一次模型更新后失效75% similarUnverifiedTraditional large model training relies heavily on manually annotated data and external corpus collection, which are typically the bottlenecks constraining iteration speed.75% similarUnverifiedAs the scale of pretrained models increases, many previously effective explicit knowledge injection techniques have gradually become ineffective or even produced negative effects.75% similarVerifiedAlignment 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.74% similarUnverifiedThe quality and diversity of training data directly affects the reliability of model outputs, and training data containing large amounts of low-quality, contradictory, or false information can cause models to learn incorrect patterns.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/930527API
curl https://kongchang.com/api/v1/knowledge/claims/930527MCP
get_claim(id=930527)