Personalized Generation In Large Model Era: A Survey

Abstract

In the era of large models, content generation is gradually shifting to Personalized Generation (PGen), tailoring content to individual preferences and needs. This paper presents the first comprehensive survey on PGen, investigating existing research in this rapidly growing field. We conceptualize PGen from a unified perspective, systematically formalizing its key components, core objectives, and abstract workflows. Based on this unified perspective, we propose a multi-level taxonomy, offering an indepth review of technical advancements, commonly used datasets, and evaluation metrics across multiple modalities, personalized contexts, and tasks. Moreover, we envision the potential applications of PGen and highlight open challenges and promising directions for future exploration. By bridging PGen research across multiple modalities, this survey serves as a valuable resource for fostering knowledge sharing and interdisciplinary collaboration, ultimately contributing to a more personalized digital landscape.

@inproceedings{xu2025pgen,
  title={Personalized Generation In Large Model Era: A Survey},
  author={Xu, Yiyan and Zhang, Jinghao and Salemi, Alireza and Hu, Xinting and Wang, Wenjie and Feng, Fuli and Zamani, Hamed and He, Xiangnan and Chua, Tat-Seng},
  booktitle={Proceedings of the 63nd Annual Meeting of the Association for Computational Linguistics},
  publisher={Association for Computational Linguistics},
  year={2025}
}
Copy

Yiyan Xu

许艺龑

Wenjie Wang

王文杰 教授

Fuli Feng

冯福利 教授

Xiangnan He

何向南 教授