用自生成数据快速个性化大模型，无需微调
Personalize Your LLM: Fake it then Align it
- 自动生成用户偏好数据，避免依赖真实数据集
- 在LaMP基准上平均性能提升40%，优于两种基线方法
- 适合需要低成本快速适配的个性化场景
个性化大型语言模型对提升用户体验至关重要。现有方法多需为每位用户微调模型，成本高昂；基于检索的方法虽更高效，但依赖高质量数据集，难以普及。为此，我们提出CHAMELEON，一种可扩展且高效的个性化方法，利用（1）自生成的个人偏好数据和（2）表示编辑技术，实现快速、低成本的模型个性化。实验表明，在包括LaMP在内的多个任务上，CHAMELEON能有效适应个人偏好，提升指令微调模型表现，相较于两种基线方法在两种模型架构上平均提升40%。
原文摘要 · Abstract (English)
Personalizing large language models (LLMs) is essential for delivering tailored interactions that improve user experience. Many existing personalization methods require fine-tuning LLMs for each user, rendering them prohibitively expensive for widespread adoption. Although retrieval-based approaches offer a more compute-efficient alternative, they still depend on large, high-quality datasets that are not consistently available for all users. To address this challenge, we propose CHAMELEON, a scalable and efficient personalization approach that uses (1) self-generated personal preference data and (2) representation editing to enable quick and cost-effective personalization. Our experiments on various tasks, including those from the LaMP personalization benchmark, show that CHAMELEON efficiently adapts models to personal preferences, improving instruction-tuned models and outperforms two personalization baselines by an average of 40% across two model architectures.
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