Resampling Benchmark for Efficient Comprehensive Evaluation of Large Vision-Language Models

Teppei Suzuki; Keisuke Ozawa

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arXiv · 2504.09979

Resampling Benchmark for Efficient Comprehensive Evaluation of Large Vision-Language Models

Teppei Suzuki·Keisuke Ozawa

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Abstract

We propose an efficient evaluation protocol for large vision-language models (VLMs). Given their broad knowledge and reasoning capabilities, multiple benchmarks are needed for comprehensive assessment, making evaluation computationally expensive. To improve efficiency, we construct a subset that yields results comparable to full benchmark evaluations. Our benchmark classification experiments reveal that no single benchmark fully covers all challenges. We then introduce a subset construction method using farthest point sampling (FPS). Our experiments show that FPS-based benchmarks maintain a strong correlation (> 0.96) with full evaluations while using only ~1\% of the data. Additionally, applying FPS to an existing benchmark improves correlation with overall evaluation results, suggesting its potential to reduce unintended dataset biases.

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Teppei Suzuki, Keisuke Ozawa. 2025-04-14. Resampling Benchmark for Efficient Comprehensive Evaluation of Large Vision-Language Models. https://arxiv.org/abs/2504.09979

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