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Personalized Generation In Large Model Era: A Survey

Paper · 2025 · Yiyan Xu, Jinghao Zhang, Alireza Salemi et al.

Summary

Work on generating content tailored to individual users has grown separately in NLP, computer vision and information retrieval. Earlier reviews each covered only one model family (LLMs, MLLMs, diffusion models) or one task, such as dialogue or role-playing. This ACL 2025 survey is presented as the first to treat Personalized Generation (PGen) as a single field that spans text, image, video, 3D, audio and cross-modal outputs.

The authors build a modality-agnostic framework with three parts:

  • Inputs: personalized contexts plus multimodal user instructions.
  • Objectives: three goals that hold across all modalities.
  • Workflow: a user-modeling stage followed by a generative-modeling stage.

On top of this framework they organize the literature into a multi-level taxonomy by modality, type of user input and task. For each modality they also catalogue common datasets and metrics, and they discuss uses in content creation and content delivery.

The paper's main value is as a shared map. It lets researchers in different communities compare techniques and see open gaps. These gaps include evaluation designed specifically for personalization, management of user data over a lifetime, and privacy.

Key points

  • Five kinds of personalized context: user profiles, user documents, user behaviors, personal face/body, and personalized subjects (e.g., pets or owned items).
  • Three shared objectives: high quality, instruction alignment, and personalization.
  • Workflow, user-modeling stage: relies on representation learning, prompt engineering, and RAG.
  • Workflow, generative-modeling stage: has three steps:
    • choosing a foundation model (LLMs, MLLMs or DMs);
    • adding a guidance mechanism, either instruction-based or structural (adapters, cross-attention);
    • selecting an optimization strategy: tuning-free, supervised fine-tuning/PEFT, or preference-based (DPO, RLHF).
  • Open problem, evaluation: standard metrics such as BLEU and CLIP-I do not measure how well outputs match a user's preferences.
  • Open problem, multimodal personalization: this remains underexplored, and unified models such as Show-o, Janus-Pro, BAGEL and GPT-4o are named as promising backbones.
  • Trustworthiness concerns: privacy (on-device, federated learning, differential privacy), fairness and bias, and safety.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓National University of SingaporeSingapore · Singapore207 works · 39,576 citations🎓University of Massachusetts AmherstAmherst Center · United States114 works · 68,731 citations🎓University of Science and Technology of ChinaHefei · China145 works · 114,409 citations🎓Nanyang Technological UniversitySingapore · Singapore184 works · 27,484 citations🎓University of Chinese Academy of SciencesBeijing · China188 works · 8,743 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksInformation Systems · 1,730 works

Cite

Cite as Yiyan Xu, Jinghao Zhang, Alireza Salemi, Xinting Hu, Wenjie Wang, Fuli Feng, Hamed Zamani, Xiangnan He and Tat-Seng Chua. 2025. Personalized Generation In Large Model Era: A Survey. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.acl-long.1201

@inproceedings{xu2025personalized,
  title = {{Personalized Generation In Large Model Era: A Survey}},
  author = {Yiyan Xu, Jinghao Zhang, Alireza Salemi, Xinting Hu, Wenjie Wang, Fuli Feng, Hamed Zamani, Xiangnan He and Tat-Seng Chua},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.1201},
  url = {https://doi.org/10.18653/v1/2025.acl-long.1201}
}

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