MIHBench: Benchmarking and Mitigating Multi-Image Hallucinations in Multimodal Large Language Models

• URL: http://arxiv.org/abs/2508.00726v1
• Date: Fri, 01 Aug 2025 15:49:29 GMT
• Title: MIHBench: Benchmarking and Mitigating Multi-Image Hallucinations in Multimodal Large Language Models
• Authors: Jiale Li, Mingrui Wu, Zixiang Jin, Hao Chen, Jiayi Ji, Xiaoshuai Sun, Liujuan Cao, Rongrong Ji,
• Abstract summary: We conduct the first systematic study of hallucinations in multi-image MLLMs.<n>We propose MIHBench, a benchmark specifically tailored for evaluating object-related hallucinations across multiple images.<n>MIHBench comprises three core tasks: Multi-Image Object Existence Hallucination, Multi-Image Object Count Hallucination, and Object Identity Consistency Hallucination.
• Score: 73.20126092411776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite growing interest in hallucination in Multimodal Large Language Models, existing studies primarily focus on single-image settings, leaving hallucination in multi-image scenarios largely unexplored. To address this gap, we conduct the first systematic study of hallucinations in multi-image MLLMs and propose MIHBench, a benchmark specifically tailored for evaluating object-related hallucinations across multiple images. MIHBench comprises three core tasks: Multi-Image Object Existence Hallucination, Multi-Image Object Count Hallucination, and Object Identity Consistency Hallucination, targeting semantic understanding across object existence, quantity reasoning, and cross-view identity consistency. Through extensive evaluation, we identify key factors associated with the occurrence of multi-image hallucinations, including: a progressive relationship between the number of image inputs and the likelihood of hallucination occurrences; a strong correlation between single-image hallucination tendencies and those observed in multi-image contexts; and the influence of same-object image ratios and the positional placement of negative samples within image sequences on the occurrence of object identity consistency hallucination. To address these challenges, we propose a Dynamic Attention Balancing mechanism that adjusts inter-image attention distributions while preserving the overall visual attention proportion. Experiments across multiple state-of-the-art MLLMs demonstrate that our method effectively reduces hallucination occurrences and enhances semantic integration and reasoning stability in multi-image scenarios.

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