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

• URL: http://arxiv.org/abs/2504.21559v1
• Date: Wed, 30 Apr 2025 11:58:30 GMT
• Title: Black-Box Visual Prompt Engineering for Mitigating Object Hallucination in Large Vision Language Models
• Abstract summary: Large Vision Language Models (LVLMs) often suffer from object hallucination, which undermines their reliability.<n>We propose Black-Box Visual Prompt Engineering (BBVPE) to identify optimal VPs that enhance LVLM responses without needing access to model internals.<n>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.
• Score: 16.743274500376657
• License: http://creativecommons.org/licenses/by/4.0/
• 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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