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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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.26878 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | J0wjh8gD84OF |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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