Unverified50% confidenceFactExact time
When the observation window is not aligned with the local crop phenology, model accuracy collapses while deterministic confidence remains high—meaning the model makes errors confidently.
1
Sources
50%
Confidence
Long-term
Relevance
10/8/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe TruthfulQA benchmark found that larger models sometimes produce more confident errors, a phenomenon known as the scaling paradox.74% similarVerified若用含测试集的全量数据计算均值和标准差，会导致预处理泄漏，使评估指标虚高，无法真实反映模型泛化能力74% similarUnverifiedThe overconfidence of a model answering directly while skipping retrieval is technically referred to as miscalibration, meaning there is a systematic deviation between confidence and actual accuracy.72% similarUnverifiedVersion drift is dangerous in evaluation scenarios because the validity of historical measurement results implicitly decays after changes to model weights, inference frameworks, or test environments72% similarUnverifiedResearch reveals that LLMs do not reliably weight and update based on evidence reliability like an ideal Bayesian agent, but instead exhibit behavioral patterns close to distributional skew, with their influence being less correlated with the true reliability of evidence than the Bayesian framework would predict.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/986742API
curl https://kongchang.com/api/v1/knowledge/claims/986742MCP
get_claim(id=986742)