AI RESEARCH
Believing without Seeing: Quality Scores for Contextualizing Vision-Language Model Explanations
arXiv CS.CL
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ArXi:2509.25844v3 Announce Type: replace When people query Vision-Language Models (VLMs) but cannot see the accompanying visual context (e.g. for blind and low-vision users), augmenting VLM predictions with natural language explanations can signal which model predictions are reliable. However, prior work has found that explanations can easily convince users that inaccurate VLM predictions are correct. To remedy undesirable overreliance on VLM predictions, we propose evaluating two complementary qualities of VLM-generated explanations via two quality scoring functions.