ZINA: Multimodal Fine-grained Hallucination Detection and Editing

• URL: http://arxiv.org/abs/2506.13130v1
• Date: Mon, 16 Jun 2025 06:27:59 GMT
• Title: ZINA: Multimodal Fine-grained Hallucination Detection and Editing
• Authors: Yuiga Wada, Kazuki Matsuda, Komei Sugiura, Graham Neubig,
• Abstract summary: Multimodal Large Language Models (MLLMs) often generate hallucinations, where the output deviates from the visual content.<n>We propose a novel task of multimodal fine-grained hallucination detection and editing for MLLMs.<n>We present ZINA, a novel method that identifies hallucinated spans at a fine-grained level, classifies their error types into six categories, and suggests appropriate refinements.
• Score: 46.2482873419289
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Large Language Models (MLLMs) often generate hallucinations, where the output deviates from the visual content. Given that these hallucinations can take diverse forms, detecting hallucinations at a fine-grained level is essential for comprehensive evaluation and analysis. To this end, we propose a novel task of multimodal fine-grained hallucination detection and editing for MLLMs. Moreover, we propose ZINA, a novel method that identifies hallucinated spans at a fine-grained level, classifies their error types into six categories, and suggests appropriate refinements. To train and evaluate models for this task, we constructed VisionHall, a dataset comprising 6.9k outputs from twelve MLLMs manually annotated by 211 annotators, and 20k synthetic samples generated using a graph-based method that captures dependencies among error types. We demonstrated that ZINA outperformed existing methods, including GPT-4o and LLama-3.2, in both detection and editing tasks.

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