NaturalBench: Evaluating Vision-Language Models on Natural Adversarial Samples

NaturalBench, a NeurIPS 2024 benchmark for evaluating vision-language models, argues that established visual question-answering benchmarks can overstate model capabilities because many questions are answerable through language knowledge or answer-distribution biases without analyzing images. It creates adversarial image-text examples by pairing visually similar images and generating human-verified questions that require opposite answers for each image, making visual input essential and preventing language-only systems from outperforming chance. Tests of more than 50 open- and closed-source models found that many performed only slightly above random guessing, while models including GPT-4o remained substantially below human performance. The benchmark highlights shortcomings in compositional reasoning, object attributes, relational and spatial understanding, logic, comparison, counting, and models’ tendency to favor particular answers regardless of image content. Its authors advocate for more balanced, vision-centered evaluations and caution against deploying current models in applications that depend on reliable spatial or visual reasoning.

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