Public Benchmark

Public benchmarks are crucial for evaluating and comparing machine learning models, particularly large language models (LLMs), but their reliability is threatened by data leakage and inconsistent evaluation methodologies. Current research focuses on developing methods to detect and mitigate benchmark contamination, improving benchmark transparency through data distribution analysis, and creating private benchmarking techniques to protect test data. These efforts aim to enhance the validity and fairness of model comparisons, ultimately improving the trustworthiness and reproducibility of research findings across various domains, including natural language processing and medical image analysis.