When large language models meet personalization: perspectives of challenges and opportunities

• DOI: 10.1007/s11280-024-01276-1
• Publication Date: 2024-06-28T13:04:45Z
• Authors: Jin Chen; Zheng Liu; Xu Huang; Chenwang Wu; Qi Liu; Gangwei Jiang; Yuanhao Pu; Yuxuan Lei; Xiaolong Chen; Xingmei Wang; Kai Zheng; Defu Lian; Enhong Chen
• Keywords: Scope (computer science); Natural language understanding

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