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On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization
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“Furthermore, standard optimization risks collapsing minority preference modes (Xiao et al., 2025).”
From The landscape of agentic reinforcement learning for LLMs: a survey (2025) · cites this paper as Xiao et al., 2025
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Cited by
The landscape of agentic reinforcement learning for LLMs: a survey
Zhang, G., Geng, H., Yu, X., Yin, Z., Zhang, Z., Tan, Z., Zhou, H., Li, Z., Xue, X., Li, Y., et al. (2025) · arXiv · doi:10.48550/arxiv.2509.02547 · Cited by 229
Self-citationA Scoping Review of LLM-as-a-Judge in Healthcare and the MedJUDGE Framework
Li, C., Akhtar, Z., Kwak, M., Ji, Y., Zhang, H., Obi, T., Ren, Y., Wu, X., Sivarajkumar, S., Lehmann, H. P., et al. (2026) · arXiv · doi:10.48550/arxiv.2604.25933 · Cited by 1
Metadata
- Authors
- Xiao, J., Li, Z., Xie, X., Getzen, E., Fang, C., Long, Q., & Su, W. J.
- Affiliations
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
- School of Data Science, The Chinese University of Hong Kong
- Department of Mathematics, National University of Singapore
- School of Intelligence Science and Technology, Peking University
- Department of Statistics and Data Science, University of Pennsylvania
- ORCID
- Su, Weijie J.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2025-10-02
- Volume
- 120
- Issue
- 552
- Pages
- 2154-2164
- ISSN
- 0162-1459
- 1537-274X
- Cited by
- 17
- References
- 5
- Language
- en
- Licence
- doi.org/10.1080/policies-text-data-mining (tdm)
- Funders
- NIH grants (R01-EB036016, RF1AG063481, U01CA274576, NSF DMS-2310679)
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