Unverified50% confidenceSolutionExact time
Unconstrained base prompts cause the model to fall back to the average-state output most common in its training data, while deeply personalized prompts can significantly improve output uniqueness.
1
Sources
50%
Confidence
Long-term
Relevance
8/11/2026
First Seen
Sources
AI伪造创造力：当每间办公室的创意都在趋同
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.79% similarUnverifiedTraditional large model training relies heavily on manually annotated data and external corpus collection, which are typically the bottlenecks constraining iteration speed.76% similarUnverifiedAlignment 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.75% similarUnverifiedOpenAI在InstructGPT论文中证明，少量高质量人类偏好数据对模型对齐的贡献远超大规模无监督训练74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/729467API
curl https://kongchang.com/api/v1/knowledge/claims/729467MCP
get_claim(id=729467)