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AT&T Chief Data Office
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MLLMs can significantly improve KB-VQA performance by first identifying entities from a limited candidate set before selecting evidence, leading to a more efficient and effective workflow.
Surprisingly, the error in estimating counts for hierarchical prefixes remains constant regardless of hierarchy height or the number of heavy hitters.
