Unverified50% confidenceOpinionExact time
As the scale of pretrained models increases, many previously effective explicit knowledge injection techniques have gradually become ineffective or even produced negative effects.
1
Sources
50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedAfter large models entered the post-pretraining era, the capability gains from simply stacking parameters have become increasingly diminishing, and vendors have turned to the post-training phase in search of breakthroughs.78% similarUnverified小模型通过知识蒸馏通常比直接在标注数据上训练精度高出2-5个百分点76% similarUnverifiedSimply 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.76% similarUnverifiedDeepMind's Chinchilla research pointed out that previous models were generally oversized and undertrained, meaning they had too many parameters but insufficient training data.76% 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.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/907940API
curl https://kongchang.com/api/v1/knowledge/claims/907940MCP
get_claim(id=907940)