Proposes Visual Fidelity and Contrastiveness scores for VLM explanations that improve user accuracy in judging prediction correctness by 11.1% without visual context on A-OKVQA, VizWiz, and MMMU-Pro.
Reframing Human- AI Collaboration for Generating Free-Text Explanations
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Believing without Seeing: Quality Scores for Contextualizing Vision-Language Model Explanations
Proposes Visual Fidelity and Contrastiveness scores for VLM explanations that improve user accuracy in judging prediction correctness by 11.1% without visual context on A-OKVQA, VizWiz, and MMMU-Pro.
- iPOE: Interpretable Prompt Optimization via Explanations