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 are typically evaluated with Visual Question Answering (VQA) tasks which assess a model's understanding of scenes. Good VQA performance is taken as evidence that the model will perform well on a broader range of tasks that require both visual and language inputs. However%2C scene-aware VQA does not fully capture input biases or assess hallucinations caused by a misalignment between modalities. To address this%2C we propose a Cross-modal Alignment Similarity Test (CAST) to probe VLMs for self-consistency across modalities. This test involves asking the models to identify similarities between two scenes through text-only%2C image-only%2C or both and then assess the truthfulness of the similarities they generate. Since there is no ground-truth to compare against%2C this evaluation does not focus on objective accuracy but rather on whether VLMs are internally consistent in their outputs. We argue that while not all self-consistent models are capable or accurate%2C all capable VLMs must be self-consistent.?quality=80&w=800)
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