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This paper introduces Mixture-of-Facets (MoF), a scalable framework for personalizing black-box LLMs by decomposing user preferences into shared latent facets rather than maintaining linearly growing user-specific parameters. By utilizing history-conditioned routing over these shared heads, the method achieves strong generalization to unseen users without requiring additional adaptation or parameter updates. This approach directly addresses the efficiency and scalability bottlenecks inherent in current personalization techniques for proprietary models.
MoF: Preference-Aware Mixture Modeling for Black-Box LLM Personalization
arXiv:2610.08330v1 Announce Type: new Abstract: Proprietary Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet aligning their outputs with diverse user preferences remains challenging. Existing personalization approaches for black-box LLMs often rely on user-specific scoring heads, causing the number of personalized parameters to grow linearly with the number of users and requiring additional adaptation for unseen users. To address these limitations, we propose Mixture-of-Facets (MoF), a scalable personalization framework for black-box LLMs that models user preferences as compositions of shared latent preference facets rather than dedicated user-specific parameters. MoF performs personalization through history-conditioned routing over shared facet heads, enabling personalization for users unseen during training without additional parameter updates. Across diverse personalization tasks, MoF delivers stronger personalization performance while maintaining a more scalable and parameter-efficient design than prior approaches. Additional analysis indicates strong generalization to unseen users.
Abstract:Proprietary Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet aligning their outputs with diverse user preferences remains challenging. Existing personalization approaches for black-box LLMs often rely on user-specific scoring heads, causing the number of personalized parameters to grow linearly with the number of users and requiring additional adaptation for unseen users. To address these limitations, we propose Mixture-of-Facets (MoF), a scalable personalization framework for black-box LLMs that models user preferences as compositions of shared latent preference facets rather than dedicated user-specific parameters. MoF performs personalization through history-conditioned routing over shared facet heads, enabling personalization for users unseen during training without additional parameter updates. Across diverse personalization tasks, MoF delivers stronger personalization performance while maintaining a more scalable and parameter-efficient design than prior approaches. Additional analysis indicates strong generalization to unseen users.
Artificial Intelligence (cs.AI)
arXiv:2610.08330 [cs.AI]
(or arXiv:2610.08330v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.08330 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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