VLMs exhibit only slight performance degradation on hallucination benchmarks when substantial image tokens are removed, with layer-wise analysis showing increased visual token similarity in deeper layers, suggesting current benchmarks inadequately test fine-grained visual grounding.
Enhancing vision-language model relia- bility with uncertainty-guided dropout decoding.Advances in Neural Information Processing Systems, 38:149193– 149218, 2025
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Seeing without Looking: Do Vision-Language Benchmarks Really Test Vision?
VLMs exhibit only slight performance degradation on hallucination benchmarks when substantial image tokens are removed, with layer-wise analysis showing increased visual token similarity in deeper layers, suggesting current benchmarks inadequately test fine-grained visual grounding.