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

• URL: http://arxiv.org/abs/2504.09979v1
• Date: Mon, 14 Apr 2025 08:43:00 GMT
• Title: Resampling Benchmark for Efficient Comprehensive Evaluation of Large Vision-Language Models
• Authors: Teppei Suzuki, Keisuke Ozawa,
• Abstract summary: We propose an efficient evaluation protocol for large vision-language models (VLMs)<n>We construct a subset that yields results comparable to full benchmark evaluations.<n>Applying FPS to an existing benchmark improves correlation with overall evaluation results.
• Score: 18.309464845180237
• License: http://creativecommons.org/licenses/by/4.0/
• 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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