A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910907572944896 |
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| author | Xie, Zhouhang Wu, Junda Shen, Yiran Xia, Yu Li, Xintong Chang, Aaron Rossi, Ryan Kumar, Sachin Majumder, Bodhisattwa Prasad Shang, Jingbo Ammanabrolu, Prithviraj McAuley, Julian |
| author_facet | Xie, Zhouhang Wu, Junda Shen, Yiran Xia, Yu Li, Xintong Chang, Aaron Rossi, Ryan Kumar, Sachin Majumder, Bodhisattwa Prasad Shang, Jingbo Ammanabrolu, Prithviraj McAuley, Julian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07070 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models Xie, Zhouhang Wu, Junda Shen, Yiran Xia, Yu Li, Xintong Chang, Aaron Rossi, Ryan Kumar, Sachin Majumder, Bodhisattwa Prasad Shang, Jingbo Ammanabrolu, Prithviraj McAuley, Julian Computation and Language 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. |
| title | A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2504.07070 |