CaptionQA is a new benchmark with 33,027 questions across natural, document, e-commerce, and embodied AI domains that measures how much utility model-generated captions retain compared to original images when used by LLMs for downstream tasks.
Object hallucination in image cap- tioning
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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.
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CaptionQA: Is Your Caption as Useful as the Image Itself?
CaptionQA is a new benchmark with 33,027 questions across natural, document, e-commerce, and embodied AI domains that measures how much utility model-generated captions retain compared to original images when used by LLMs for downstream tasks.
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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.