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cs.CL2025
Detecting Data Contamination from Reinforcement Learning Post-training for Large Language Models
Yongding Tao, Tian Wang, Yihong Dong +4
Data contamination poses a significant threat to the reliable evaluation of Large Language Models (LLMs). This issue arises when benchmark samples may inadvertently appear in train…
cs.CL2025
STEM: Efficient Relative Capability Evaluation of LLMs through Structured Transition Samples
Haiquan Hu, Jiazhi Jiang, Shiyou Xu +2
Evaluating large language models (LLMs) has become increasingly challenging as model capabilities advance rapidly. While recent models often achieve higher scores on standard bench…