Zooming from Context to Cue: Hierarchical Preference Optimization for Multi-Image MLLMs

• URL: http://arxiv.org/abs/2505.22396v1
• Date: Wed, 28 May 2025 14:24:02 GMT
• Title: Zooming from Context to Cue: Hierarchical Preference Optimization for Multi-Image MLLMs
• Authors: Xudong Li, Mengdan Zhang, Peixian Chen, Xiawu Zheng, Yan Zhang, Jingyuan Zheng, Yunhang Shen, Ke Li, Chaoyou Fu, Xing Sun, Rongrong Ji,
• Abstract summary: We propose Context-to-Cue Direct Preference Optimization (CcDPO), a multi-level preference optimization framework.<n>CcDPO enhances per-image perception in multi-image settings by zooming into visual clues -- from sequential context to local details.<n> Experiments show that CcDPO significantly reduces hallucinations and yields consistent performance gains.
• Score: 74.74767980885758
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-modal Large Language Models (MLLMs) excel at single-image tasks but struggle with multi-image understanding due to cross-modal misalignment, leading to hallucinations (context omission, conflation, and misinterpretation). Existing methods using Direct Preference Optimization (DPO) constrain optimization to a solitary image reference within the input sequence, neglecting holistic context modeling. We propose Context-to-Cue Direct Preference Optimization (CcDPO), a multi-level preference optimization framework that enhances per-image perception in multi-image settings by zooming into visual clues -- from sequential context to local details. It features: (i) Context-Level Optimization : Re-evaluates cognitive biases underlying MLLMs' multi-image context comprehension and integrates a spectrum of low-cost global sequence preferences for bias mitigation. (ii) Needle-Level Optimization : Directs attention to fine-grained visual details through region-targeted visual prompts and multimodal preference supervision. To support scalable optimization, we also construct MultiScope-42k, an automatically generated dataset with high-quality multi-level preference pairs. Experiments show that CcDPO significantly reduces hallucinations and yields consistent performance gains across general single- and multi-image tasks.

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