QuizRank: Picking Images by Quizzing VLMs
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911165008838656 |
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| author | Ji, Tenghao Adar, Eytan |
| author_facet | Ji, Tenghao Adar, Eytan |
| contents | Images play a vital role in improving the readability and comprehension of Wikipedia articles by serving as `illustrative aids.' However, not all images are equally effective and not all Wikipedia editors are trained in their selection. We propose QuizRank, a novel method of image selection that leverages large language models (LLMs) and vision language models (VLMs) to rank images as learning interventions. Our approach transforms textual descriptions of the article's subject into multiple-choice questions about important visual characteristics of the concept. We utilize these questions to quiz the VLM: the better an image can help answer questions, the higher it is ranked. To further improve discrimination between visually similar items, we introduce a Contrastive QuizRank that leverages differences in the features of target (e.g., a Western Bluebird) and distractor concepts (e.g., Mountain Bluebird) to generate questions. We demonstrate the potential of VLMs as effective visual evaluators by showing a high congruence with human quiz-takers and an effective discriminative ranking of images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15059 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QuizRank: Picking Images by Quizzing VLMs Ji, Tenghao Adar, Eytan Human-Computer Interaction Computer Vision and Pattern Recognition Images play a vital role in improving the readability and comprehension of Wikipedia articles by serving as `illustrative aids.' However, not all images are equally effective and not all Wikipedia editors are trained in their selection. We propose QuizRank, a novel method of image selection that leverages large language models (LLMs) and vision language models (VLMs) to rank images as learning interventions. Our approach transforms textual descriptions of the article's subject into multiple-choice questions about important visual characteristics of the concept. We utilize these questions to quiz the VLM: the better an image can help answer questions, the higher it is ranked. To further improve discrimination between visually similar items, we introduce a Contrastive QuizRank that leverages differences in the features of target (e.g., a Western Bluebird) and distractor concepts (e.g., Mountain Bluebird) to generate questions. We demonstrate the potential of VLMs as effective visual evaluators by showing a high congruence with human quiz-takers and an effective discriminative ranking of images. |
| title | QuizRank: Picking Images by Quizzing VLMs |
| topic | Human-Computer Interaction Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.15059 |