A multimodal cross-modal explainable retrieval-augmented generation framework for hallucination-grounded visual question answering
Recent multimodal large language models (MLLMs) have achieved remarkable performance on visual question answering (VQA) and multimodal reasoning tasks. But one overlooked failure case stubbornly persists: when answers require simultaneous synthesis of evidence from natural images, free-form text, and structured tables,...
Babasaheb Satpute, Wasudeo P. Rahane, Rashmi B. Kale et al.
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