Unverified50% confidenceBenchmarkExact time
The study found that models' accuracy on semantically plausible distorted statements is actually higher than their performance on meaningless random replacements.
1
Sources
50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
Related Entities
Related Claims
Unverified泰特洛克的研究得出结论:专家预测的准确率仅略高于随机猜测,甚至不如简单的统计基准线77% similarUnverified斯坦福大学研究发现,在推理链关键步骤注入错误信息对最终答案的影响远小于预期74% similarUnverifiedIn class-imbalance scenarios, a model can achieve seemingly impressive accuracy by always predicting the majority class, a phenomenon known as the Accuracy Paradox.74% similarUnverifiedThe academic community refers to the phenomenon of significant deviation between a model's confidence and its actual accuracy as Poor Calibration.73% similarVerifiedWhen samples are severely imbalanced, Accuracy becomes misleading—a model that predicts everyone as healthy can still achieve an accuracy of over 99%.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/893871API
curl https://kongchang.com/api/v1/knowledge/claims/893871MCP
get_claim(id=893871)