Efficient Benchmarking Is Just Feature Selection and Multiple Regression

Researchers demonstrate that efficient LLM benchmarking can be substantially improved by treating it as a multiple regression problem with kernel ridge regression and applying minimum redundancy maximum relevance (mRMR) feature selection. The approach achieves lower prediction errors and faster computation than existing methods while maintaining consistency across different data splits.

This research addresses a critical pain point in AI development: the computational expense of evaluating large language models across comprehensive benchmarks. As LLMs grow larger and more complex, running full benchmark suites becomes prohibitively expensive. The authors reframe efficient benchmarking as a statistical problem rather than a specialized ML challenge, revealing that simpler, well-established techniques outperform more complex competitors.

The key innovation lies in combining kernel ridge regression with mRMR feature selection. Traditional efficient benchmarking methods attempt to predict full benchmark scores from subset performance using probabilistic models or clustering algorithms. This research demonstrates that such complexity is unnecessary—kernel ridge regression provides superior prediction accuracy while mRMR selects more informative question subsets faster than competing approaches. The consistency across random seeds and data splits is particularly valuable for reproducible research.

For AI developers and researchers, this work directly reduces computational barriers to model evaluation. Faster, cheaper benchmarking enables more frequent iteration cycles and broader comparison of models across different architectures. This democratizes LLM development by making evaluation more accessible to resource-constrained teams.

The practical implications extend beyond efficiency gains. More reliable prediction methods strengthen the benchmarking process itself, reducing noise in performance comparisons. As the field moves toward evaluating increasingly specialized models and fine-tuned variants, efficient benchmarking becomes essential infrastructure. The open-source code release ensures rapid adoption, potentially becoming standard practice across the research community.

• →Kernel ridge regression substantially improves prediction accuracy compared to existing efficient benchmarking methods across multiple benchmarks.
• →mRMR feature selection identifies more informative question subsets while executing significantly faster than probabilistic or clustering-based alternatives.
• →The approach maintains consistency in question selection across different random seeds and training data splits, supporting reproducible research.
• →This reframing of benchmarking as feature selection and regression simplifies the problem while achieving better empirical results.
• →Open-source implementation enables rapid adoption as standard practice in the LLM research community.