Unverified70% confidenceFactExact time
Research published in 2023, such as "The Curse of Recursion," shows that when models are repeatedly trained on their own generated data, the data distribution gradually degrades, diversity continuously decreases, and model capabilities systematically decline (model collapse).
2
Sources
70%
Confidence
Long-term
Relevance
8/25/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedDuring model collapse, the model successively experiences a reduction in output diversity, loss of minority categories and edge knowledge, and ultimately produces meaningless or highly repetitive outputs.80% similarUnverified当生成模型反复在自身生成的数据上训练时，输出分布会逐渐退化，低概率事件被系统性遗忘77% similarUnverifiedThe mechanism of model collapse is distribution drift caused by recursive training, a learning-level problem; whereas the spread of false claims through document copying is content-level contamination, and the two have different intervention methods.74% similarUnverifiedThe understanding of text-to-image models is essentially large-scale statistical association, and generation quality drops significantly when dealing with long-tail concepts that appear rarely in the training set.74% similarVerifiedThe training data of detection models perpetually lags behind the output distribution of the latest generation technology, a problem referred to in academia as "distribution shift."72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/799917API
curl https://kongchang.com/api/v1/knowledge/claims/799917MCP
get_claim(id=799917)