Multi-Stakeholder LLM Alignment: Decomposing Estimation from Aggregation
The paper discusses the challenges of aligning large language models (LLMs) with multiple stakeholders who have conflicting preferences. It highlights the issues of utility estimation and aggregation that can lead to unstable outcomes. The authors propose a new method, DecompR, to improve the reliability of stakeholder satisfaction assessments.
- ▪Multi-stakeholder tasks require outputs that satisfy conflicting user preferences.
- ▪The paper identifies issues with holistic LLM judges that conflate utility estimation and aggregation.
- ▪The proposed method, DecompR, aims to reduce estimation noise and improve stakeholder satisfaction.
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Computer Science > Artificial Intelligence arXiv:2605.26878 (cs) [Submitted on 26 May 2026] Title:Multi-Stakeholder LLM Alignment: Decomposing Estimation from Aggregation Authors:Lulu Zheng, Wenjin Yang, Xiangwen Zhang, Rong Yin, Yulan Hu, Zheng Pan, Xin Li View a PDF of the paper titled Multi-Stakeholder LLM Alignment: Decomposing Estimation from Aggregation, by Lulu Zheng and 6 other authors View PDF HTML (experimental) Abstract:Multi-stakeholder tasks require one output to satisfy users with conflicting preferences. Holistic LLM judges conflate utility estimation and utility aggregation, yielding unstable implicit weights.
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