Abstract
Personalization is becoming indispensable for LLMs to align with individual user preferences and needs. Yet current approaches are often computationally expensive, data-intensive, susceptible to catastrophic forgetting, and prone to performance degradation in multi-turn interactions or when handling implicit queries. To address these challenges, we conceptualize personalization as a model editing task and introduce Personalization Editing, a framework that applies localized edits guided by clustered preference representations. This design enables precise preference-aligned updates while preserving overall model capabilities. In addition, existing personalization benchmarks frequently rely on persona-based dialogs between LLMs rather than user-LLM interactions, or focus primarily on stylistic imitation while neglecting information-seeking tasks that require accurate recall of user-specific preferences. We introduce User Preference Question Answering (UPQA), a short-answer QA dataset constructed from in-situ user queries with varying levels of difficulty. Unlike prior benchmarks, UPQA directly evaluates a model's ability to recall and apply specific user preferences. Across experimental settings, Personalization Editing achieves higher editing accuracy and greater computational efficiency than fine-tuning, while outperforming prompting-based baselines in multi-turn conversations and implicit preference questions settings.
Abstract (translated)
个性化对于大型语言模型（LLM）来说正变得不可或缺，以使其能够与个人用户的偏好和需求保持一致。然而，目前的方法往往计算成本高昂、数据密集型，并且容易发生灾难性遗忘，在处理多轮交互或隐式查询时性能会下降。为了解决这些问题，我们将个性化视为一个模型编辑任务，并引入了“Personalization Editing”框架，该框架通过集群偏好数字表示进行局部编辑指导。这种设计能够在保持整体模型能力的同时实现精准的偏好对齐更新。 此外，现有的个性化基准测试通常依赖于基于角色的人机对话，而不是用户与LLM之间的实际交互，或者它们主要集中在风格模仿上，而忽略了需要准确回忆出特定用户偏好的信息查找任务。我们引入了“User Preference Question Answering”（UPQA），这是一个从现场用户的查询中构建的短答案问答数据集，并且具有不同难度级别的问题。与先前的基准测试相比，UPQA直接评估模型回忆和应用具体用户偏好的能力。 在各种实验设置下，“Personalization Editing”的编辑精度高于微调方法，并且计算效率更高，在多轮对话和隐式偏好查询场景中优于基于提示的方法。
URL
https://arxiv.org/abs/2512.13676
