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arXiv · 2606.01044

Ask4VG: Risk-Aware Question Selection for Reducing Prior-Driven Answers in Medical VQA

Xiaorong Zhu·Qiang Li·Zibo Xu·Weijie Wang·Weizhi Nie
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Abstract

Medical visual question answering requires models to ground their responses in image evidence, because visually unsupported answers can mislead downstream interpretation. However, many medical VQA questions are generic, template-like, or highly similar in form, which can encourage models to learn question-answer shortcuts instead of image-dependent reasoning and thereby increase the risk of hallucinated responses. We propose Ask4VG, a label-free pilot framework for risk-aware question selection. Ask4VG estimates question-induced hallucination risk through counterfactual visual probing: the same question is asked under the original image, a perturbed image, a blank image, and a mismatched image, and the resulting answer relations are converted into weak supervision for a counterfactual risk estimator. The learned estimator then reranks candidate question rewrites to favor intent-preserving questions that are less invariant to missing or mismatched visual evidence before final answer generation. On VQA-RAD with Qwen2-VL-2B-Instruct, prompt-only rewriting increases counterfactual risk, whereas predicted-risk reranking reduces held-out risk from 0.658 to 0.623 and improves exact accuracy from 0.337 to 0.356. A 300-sample PMC-VQA external check shows the same direction of risk reduction with a small accuracy gain. These results suggest that question selection is a promising complement to response-level hallucination mitigation for reliable medical VQA.

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Xiaorong Zhu, Qiang Li, Zibo Xu, Weijie Wang, Weizhi Nie. 2026-05-31. Ask4VG: Risk-Aware Question Selection for Reducing Prior-Driven Answers in Medical VQA. https://arxiv.org/abs/2606.01044

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