Group Preference Collapse in Personalized Multimodal Large Language Models
Personalized multimodal large language models (MLLMs) aim to generate user-specific responses, but existing methods mainly rely on profile-level information and overlook diverse user preferences. We identify group preference collapse, where multi-user personalized MLLMs become insensitive to individual preferences and drift toward dominant population-level choices due to suppressed preference signals and unreliable preference use during generation. We propose PrefMoE, a preference-centric framework that separates stable profile information from preference-related representations. PrefMoE decomposes preferences into shared prototypes and personalized residuals, preserves individualized res...
| Authors | Fan Lyu, Wenqi Zhang, Joost van de Weijer |
|---|---|
| Primary category | cs.AI |
| Published | 12 June 2026 |
| Last updated at source | 30 September 2026 |
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