Beyond Text-Dominance: Understanding Modality Preference of Omni-modal Large Language Models

Xinru Yan, Boxi Cao, Yaojie Lu, Hongyu Lin, Weixiang Zhou, Le Sun, Xianpei Han

Beyond Text-Dominance: Understanding Modality Preference of Omni-modal Large Language Models: 4 upvotes on Hugging Face Daily Papers, #23 of 49 papers on 2026-04-21. Day-by-day upvote history.

Native Omni-modal Large Language Models (OLLMs) have shifted from pipeline architectures to unified representation spaces. However, this native integration gives rise to a critical yet underexplored phenomenon: modality preference. To bridge this gap, we first systematically quantify modality preference of OLLMs using a newly-curated conflict-based benchmark and the modality selection rate metric. Our evaluation of ten representative OLLMs reveals a notable paradigm shift: unlike the ``text-dominance'' of traditional VLMs, most OLLMs exhibit a pronounced visual preference. To further understand the underlying mechanism, we conduct layer-wise probing and demonstrate that such modality preference is not static but emerges progressively in the mid-to-late layers. Building upon these insights, we leverage these internal signals to diagnose cross-modal hallucinations, achieving competitive performance across three downstream multi-modal benchmarks without task-specific data. Our work provides both a mechanistic understanding and a practical tool for building more trustworthy OLLMs. Our code and related resources are publicly available at: https://github.com/icip-cas/OmniPreference

Paper page on Hugging Face · arXiv

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