A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models

Zhouhang Xie, Junda Wu, Yiran Shen, Yu Xia, Xintong Li +7 more
4/9/2025
cs.CL

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Abstract

Personalized preference alignment for large language models (LLMs), the process of tailoring LLMs to individual users' preferences, is an emerging research direction spanning the area of NLP and personalization. In this survey, we present an analysis of works on personalized alignment and modeling for LLMs. We introduce a taxonomy of preference alignment techniques, including training time, inference time, and additionally, user-modeling based methods. We provide analysis and discussion on the strengths and limitations of each group of techniques and then cover evaluation, benchmarks, as well as open problems in the field.

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Cite this paper

@article{xie2025survey,
  title  = {A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models},
  author = {Zhouhang Xie and Junda Wu and Yiran Shen and Yu Xia and Xintong Li and Aaron Chang and Ryan Rossi and Sachin Kumar and Bodhisattwa Prasad Majumder and Jingbo Shang and Prithviraj Ammanabrolu and Julian McAuley},
  year   = {2025},
  eprint = {2504.07070},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.07070v1}
}

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