AI RESEARCH
Multi-Stakeholder LLM Alignment: Decomposing Estimation from Aggregation
arXiv CS.AI
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ArXi:2605.26878v1 Announce Type: new 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. We show empirically and theoretically that this aggregation-specific \emph{weighting noise} can create large score shifts when stakeholder satisfaction is dispersed; in our experiments, these weight-induced shifts also increase with stakeholder count.