A gradient-attention explainability method produces sequence-level visual and textual saliency maps for free-form answers from large vision-language models, with stronger human-attention alignment and faithfulness than prior baselines.
Generic attention- model explainability for interpreting bi-modal and encoder- decoder transformers
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GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models
A gradient-attention explainability method produces sequence-level visual and textual saliency maps for free-form answers from large vision-language models, with stronger human-attention alignment and faithfulness than prior baselines.