Believing without Seeing: Quality Scores for Contextualizing Vision-Language Model Explanations

• URL: http://arxiv.org/abs/2509.25844v1
• Date: Tue, 30 Sep 2025 06:34:21 GMT
• Title: Believing without Seeing: Quality Scores for Contextualizing Vision-Language Model Explanations
• Authors: Keyu He, Tejas Srinivasan, Brihi Joshi, Xiang Ren, Jesse Thomason, Swabha Swayamdipta,
• Abstract summary: We propose evaluating two complementary qualities of VLM-generated explanations via two quality scoring functions.<n>We conduct a user study in which participants have to decide whether a VLM prediction is accurate without viewing its visual context.<n>We observe that showing our quality scores alongside VLM explanations improves participants' accuracy at predicting VLM correctness by 11.1%.
• Score: 41.09442370052903
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. We propose Visual Fidelity, which captures how faithful an explanation is to the visual context, and Contrastiveness, which captures how well the explanation identifies visual details that distinguish the model's prediction from plausible alternatives. On the A-OKVQA and VizWiz tasks, these quality scoring functions are better calibrated with model correctness than existing explanation qualities. We conduct a user study in which participants have to decide whether a VLM prediction is accurate without viewing its visual context. We observe that showing our quality scores alongside VLM explanations improves participants' accuracy at predicting VLM correctness by 11.1%, including a 15.4% reduction in the rate of falsely believing incorrect predictions. These findings highlight the utility of explanation quality scores in fostering appropriate reliance on VLM predictions.

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