Unverified50% confidenceCautionExact time
LLM推荐基于训练数据的截止时间，对新发售的游戏可能缺乏了解，且无法像协同过滤那样实时捕捉群体行为趋势
1
Sources
50%
Confidence
Long-term
Relevance
8/29/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedRAG can supplement knowledge beyond the training data cutoff date in real time, avoiding expensive full fine-tuning of the model.70% 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.68% similarUnverifiedPre-training data for large models has a clear cutoff date, the model internally contains no enterprise private data, and hallucinations occur when directly queried about enterprise private information.68% similarUnverifiedDuring the pre-training phase, LLMs optimize token prediction probability through maximum likelihood estimation, lacking an inherent mechanism to 'know what they don't know,' and there is no necessary correlation between confidence and accuracy.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/816380API
curl https://kongchang.com/api/v1/knowledge/claims/816380MCP
get_claim(id=816380)