The advent of large language models marks a revolutionary breakthrough in artificial intelligence. With the unprecedented scale of training and model parameters, the capability of large language models has been dramatically improved, leading to human-like performances in understanding, language synthesizing, and common-sense reasoning, etc. Such a major leap-forward in general AI capacity will change the pattern of how personalization is conducted. For one thing, it will reform the way of interaction between humans and personalization systems. Instead of being a passive medium of information filtering, large language models present the foundation for active user engagement. On top of such a new foundation, user requests can be proactively explored, and user's required information can be delivered in a natural and explainable way. For another thing, it will also considerably expand the scope of personalization, making it grow from the sole function of collecting personalized information to the compound function of providing personalized services. By leveraging large language models as general-purpose interface, the personalization systems may compile user requests into plans, calls the functions of external tools to execute the plans, and integrate the tools' outputs to complete the end-to-end personalization tasks. Today, large language models are still being developed, whereas the application in personalization is largely unexplored. Therefore, we consider it to be the right time to review the challenges in personalization and the opportunities to address them with LLMs. In particular, we dedicate this perspective paper to the discussion of the following aspects: the development and challenges for the existing personalization system, the newly emerged capabilities of large language models, and the potential ways of making use of large language models for personalization.

翻译：大型语言模型的出现标志着人工智能领域的革命性突破。凭借前所未有的训练规模和模型参数量，大型语言模型的能力得到了显著提升，在理解、语言合成和常识推理等方面展现出类人性能。这一通用人工智能能力的重大飞跃将改变个性化实现模式。一方面，它将重塑人类与个性化系统之间的交互方式。大型语言模型不再是被动信息过滤媒介，而是成为主动用户参与的基础。在这一全新基础之上，可以主动探索用户需求，并以自然且可解释的方式传递用户所需信息。另一方面，它还极大拓展了个性化的范畴，使其从单纯收集个性化信息的功能，发展为提供个性化服务的复合功能。通过将大型语言模型作为通用接口，个性化系统可将用户请求编译为计划，调用外部工具功能执行该计划，并整合工具输出以完成端到端的个性化任务。目前，大型语言模型仍在发展之中，而其在个性化领域的应用尚待深入探索。因此，我们认为此刻正是审视个性化面临的挑战，以及利用大型语言模型应对这些挑战的机遇的正确时机。具体而言，我们以此篇观点论文致力于探讨以下方面：现有个性化系统的发展与挑战、大型语言模型新涌现的能力，以及利用大型语言模型实现个性化的潜在途径。