用进化算法实现隐私保护的个性化模型融合，提升用户偏好捕捉能力。
Personalized Language Models via Privacy-Preserving Evolutionary Model Merging
- 采用无梯度进化算法直接优化任务性能
- 在LaMP基准上任务性能最高提升45%
- 兼顾隐私保护与用户偏好表达，适合数据敏感场景
语言模型个性化旨在适应单个用户或用户群体的行为。提示法将偏好融入查询，训练法则将其编码进模型参数。在数据有限的情况下，模型融合也被用于个性化。然而，现有方法往往无法直接优化特定任务性能，且缺乏明确的隐私保护机制。为此，我们提出基于进化算法的隐私保护模型融合方法（PriME），利用无梯度方法直接优化实用性，同时降低隐私风险。通过将隐私保护纳入优化目标，PriME生成的个性化模块能有效捕捉目标用户偏好，同时减少数据共享用户的隐私泄露风险。在LaMP基准上的实验表明，PriME持续优于多种基线方法，任务性能最高提升45%。进一步分析显示，相比先前最先进方法，PriME在隐私-效用权衡上表现更优，对成员推断攻击更具鲁棒性，且更能准确捕捉用户偏好。
原文摘要 · Abstract (English)
Personalization in language models aims to tailor model behavior to individual users or user groups. Prompt-based methods incorporate user preferences into queries, while training-based methods encode them into model parameters. Model merging has also been explored for personalization under limited data. However, existing methods often fail to directly optimize task-specific utility and lack explicit mechanisms for privacy preservation. To address the limitations, we propose Privacy-Preserving Model Merging via Evolutionary Algorithms (PriME), a novel personalization approach that employs gradient-free methods to directly optimize utility while reducing privacy risks. By integrating privacy preservation into the optimization objective, PriME creates personalized modules that effectively capture target user preferences while minimizing privacy risks for data-sharing users. Experiments on the LaMP benchmark show that PriME consistently outperforms a range of baselines, achieving up to a 45% improvement in task performance. Further analysis demonstrates that PriME achieves a superior privacy-utility trade-off compared to a prior state-of-the-art, with enhanced robustness to membership inference attacks and greater utility in capturing user preferences.
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