arXiv Artificial Intelligence

Group Preference Collapse in Personalized Multimodal Large Language Models

1 hour ago 01.10.2026 07:00 👁 1 views
Group Preference Collapse in Personalized Multimodal Large Language Models

Quick summary

arXiv:2607.22603v2 Announce Type: replace Abstract: 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 fr

Key takeaways

  • arXiv:2607.22603v2 Announce Type: replace Abstract: 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 fr

Why it matters

This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗