MoF：面向黑盒大语言模型个性化的偏好感知混合建模
MoF: Preference-Aware Mixture Modeling for Black-Box LLM Personalization
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中文总结 AI 辅助
针对黑盒大语言模型个性化中参数随用户线性增长及新用户适配难的问题，提出MoF框架，将偏好建模为共享潜在面组合，通过历史条件路由实现无需额外参数更新的高效个性化，性能优于现有方法。
中文摘要 AI 辅助
专有大型语言模型（LLM）在广泛的任务中展现出卓越的能力，但将其输出与多样化的用户偏好对齐仍然具有挑战性。现有的黑盒LLM个性化方法通常依赖于用户特定的评分头，导致个性化参数数量随用户数量线性增长，并且需要对未见过的用户进行额外适配。为解决这些局限性，我们提出了多面体混合（MoF），一种可扩展的黑盒LLM个性化框架，该框架将用户偏好建模为共享潜在偏好面的组合，而非专用的用户特定参数。MoF通过基于历史条件的路由在共享面头上执行个性化，使得训练期间未见过的用户无需额外参数更新即可实现个性化。在多样化的个性化任务中，MoF在保持比先前方法更可扩展且参数效率更高的设计的同时，实现了更强的个性化性能。额外分析表明其对未见过的用户具有强大的泛化能力。
英文摘要
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.
发表机构
- Korea University（高丽大学）
机构由 AI 辅助整理，请以论文原文为准。
