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arXiv · 2504.21559

Black-Box Visual Prompt Engineering for Mitigating Object Hallucination in Large Vision Language Models

Sangmin Woo·Kang Zhou·Yun Zhou·Shuai Wang·Sheng Guan·Haibo Ding·Lin Lee Cheong
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Abstract

Large Vision Language Models (LVLMs) often suffer from object hallucination, which undermines their reliability. Surprisingly, we find that simple object-based visual prompting -- overlaying visual cues (e.g., bounding box, circle) on images -- can significantly mitigate such hallucination; however, different visual prompts (VPs) vary in effectiveness. To address this, we propose Black-Box Visual Prompt Engineering (BBVPE), a framework to identify optimal VPs that enhance LVLM responses without needing access to model internals. Our approach employs a pool of candidate VPs and trains a router model to dynamically select the most effective VP for a given input image. This black-box approach is model-agnostic, making it applicable to both open-source and proprietary LVLMs. Evaluations on benchmarks such as POPE and CHAIR demonstrate that BBVPE effectively reduces object hallucination.

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Sangmin Woo, Kang Zhou, Yun Zhou, Shuai Wang, Sheng Guan, Haibo Ding, Lin Lee Cheong. 2025-04-30. Black-Box Visual Prompt Engineering for Mitigating Object Hallucination in Large Vision Language Models. https://arxiv.org/abs/2504.21559

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