Unverified50% confidenceFactExact time
Early evaluation benchmarks such as MMLU and HellaSwag have already revealed issues such as data contamination and saturation effects.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
9/9/2026
First Seen
Valid until: 12/8/2026
Sources
Related Entities
Related Claims
UnverifiedNLP基准数据集被广泛使用后会出现数据污染（Data Contamination）现象，导致基准分数虚高71% similarUnverifiedBenchmarks for evaluating large models such as MMLU, HumanEval, and GSM8K have limitations including data contamination, overfitting, and insufficient ecological validity.71% similarUnverifiedData contamination refers to a situation where a model has already seen data from the test set during training, causing benchmark scores to be inflated.68% similarUnverified基准过拟合和数据污染会导致模型通过记忆而非真正理解获得高分，使公开基准分数可信度存疑67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/884353API
curl https://kongchang.com/api/v1/knowledge/claims/884353MCP
get_claim(id=884353)