arXiv:2605.25773v1 Announce Type: cross Abstract: Efficient benchmarking techniques aim to lower the computational cost of evaluating LLMs by predicting full benchmark scores using only a subset of a benchmark's questions. By reframing this problem as an instance of multiple regression with feature selection, we find that existing efficient benchmarking methods can be greatly improved by simply using kernel ridge regression at the prediction stage. Additionally, using an information-theoretic feature-selection algorithm called minimum redundancy maximum relevance (mRMR), we can further improve
The proliferation of Large Language Models and the increasing computational demands of benchmarking them necessitate more efficient evaluation methods now, both for academic research and commercial deployment.
Improving the efficiency of LLM benchmarking directly impacts the speed of AI development and deployment by reducing resource consumption, making advanced AI more accessible and cheaper to evaluate.
Existing efficient benchmarking methods can be significantly improved through the application of established machine learning techniques like kernel ridge regression and information-theoretic feature selection (mRMR) for LLMs.
- · AI researchers
- · LLM developers
- · Cloud computing providers (reduced egress costs)
- · AI startups
- · Inefficient benchmarking approaches
Reduced computational costs and time for evaluating LLMs, accelerating the development cycle.
Faster iteration and deployment of more advanced and specialized AI models across various industries.
Lower barriers to entry for developing and deploying sophisticated AI, potentially democratizing access to powerful models.
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Read at arXiv cs.CL