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arXiv · 2508.00726

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

Jiale Li·Mingrui Wu·Zixiang Jin·Hao Chen·Jiayi Ji·Xiaoshuai Sun·Liujuan Cao·Rongrong Ji
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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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Jiale Li, Mingrui Wu, Zixiang Jin, Hao Chen, Jiayi Ji, Xiaoshuai Sun, Liujuan Cao, Rongrong Ji. 2025-08-01. MIHBench: Benchmarking and Mitigating Multi-Image Hallucinations in Multimodal Large Language Models. https://doi.org/10.1145/3746027.3754993

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